!pip install tensorflow
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Installing collected packages: namex, libclang, flatbuffers, termcolor, tensorflow-io-gcs-filesystem, tensorboard-data-server, protobuf, optree, opt-einsum, ml-dtypes, h5py, grpcio, google-pasta, gast, astunparse, absl-py, tensorboard, rich, keras, tensorflow-intel, tensorflow
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!pip install tensorflow-recommenders
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import pandas as pd
import numpy as np
import re
import matplotlib.pyplot as plt
import plotly.express as px
import pandas as pd
import tensorflow as tf
import tensorflow as tf
from tensorflow.keras.layers import StringLookup, Embedding, LayerNormalization
import tensorflow_recommenders as tfrs
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from sklearn.metrics import confusion_matrix
df = pd.read_csv("C:\\Users\\sa2259\\OneDrive - University of Sussex\\Desktop\\project final\\Recommendation System\\Recommendation_data_retail.csv")
df.head()
| Trx_id | Timestamp | Member_id | Item_id | Item_name | Item_subclass | Item_subdepartment | Itm_qty | Item_price | Total_price | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 173131329 | 2022-11-21 00:02:36.017 | 5136784 | 1260 | Tomato /kg | tomato | fresh vegetable | 1 | 10.665 | 10.665 |
| 1 | 173131329 | 2022-11-21 00:02:36.017 | 5136784 | 50668 | Lamb Weston Crunchy Seasoned Twisters 2.5 kg | spiced fries-onion | frozen food | 1 | 26.550 | 26.550 |
| 2 | 173131329 | 2022-11-21 00:02:36.017 | 5136784 | 2116 | Orange Juice /Kg | banana african | fresh fruits | 1 | 24.273 | 24.273 |
| 3 | 173131329 | 2022-11-21 00:02:36.017 | 5136784 | 73732 | Pinar Shredded Mozzarella Cheese 500g | shredded mozzarela cheese | cheese | 2 | 37.800 | 75.600 |
| 4 | 173131329 | 2022-11-21 00:02:36.017 | 5136784 | 298472 | Radwa Fried Chicken 900g | chicken drumsticks | frozen food | 1 | 28.575 | 28.575 |
# Assuming unique_items_df has already been created
unique_items_df = df[['Item_id', 'Item_name']].drop_duplicates().reset_index(drop=True)
# Step 1: Group by 'Item_name' and count the number of unique 'Item_id's
item_name_counts = unique_items_df.groupby('Item_name')['Item_id'].nunique().reset_index(name='unique_item_id_count')
# Step 2: Filter the 'Item_name's that have more than one unique 'Item_id'
item_names_with_multiple_ids = item_name_counts[item_name_counts['unique_item_id_count'] > 1]
# To get the item names as a list or dataframe
item_names_list = item_names_with_multiple_ids['Item_name'].tolist()
item_names_df = item_names_with_multiple_ids[['Item_name']]
# If you want to get the original rows from unique_items_df for these Item_name's
filtered_df = unique_items_df[unique_items_df['Item_name'].isin(item_names_list)]
item_names_with_multiple_ids
| Item_name | unique_item_id_count | |
|---|---|---|
| 669 | 1:16 Friction Construction Truck With Light & ... | 2 |
| 30009 | Abu Bint Golden Parboiled Chopstick Rice 10kg | 2 |
| 30613 | Al Manseb Natural Honey 500 g | 2 |
| 31005 | Alali Vanilla Powder 12X20g | 2 |
| 31520 | American Garden Iodized Table Salt 26 Oz | 2 |
| ... | ... | ... |
| 46805 | Whiskas Cat Food 85 g | 2 |
| 46808 | Whiskas Dry Cat Food 1.2Kg | 2 |
| 46810 | Whiskas Dry Cat Food 480g | 2 |
| 46814 | Whiskas Wet Cat Food 12*70g | 2 |
| 47002 | Zucchini (Tray) | 2 |
109 rows × 2 columns
We realize that 109 item_names are tagged with multiple Item_Ids, which could be due to system tagging based on barcode or anything of that sort. So for better embeddings in later stages we'll drop Item_name column and consider Item_Id to be unique Identifier for Items.
# Assuming item_names_with_multiple_ids is already defined
# Plot histogram
plt.figure(figsize=(10, 6))
plt.hist(item_names_with_multiple_ids['unique_item_id_count'], bins=range(1, item_names_with_multiple_ids['unique_item_id_count'].max() + 2), edgecolor='black')
plt.xlabel('Number of Unique Item IDs')
plt.ylabel('Frequency')
plt.title('Histogram of Item Names with Multiple Unique Item IDs')
plt.grid(True)
plt.show()
df1 = df.copy()
df1.drop(columns='Item_name',inplace=True)
df1.head()
| Trx_id | Timestamp | Member_id | Item_id | Item_subclass | Item_subdepartment | Itm_qty | Item_price | Total_price | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 173131329 | 2022-11-21 00:02:36.017 | 5136784 | 1260 | tomato | fresh vegetable | 1 | 10.665 | 10.665 |
| 1 | 173131329 | 2022-11-21 00:02:36.017 | 5136784 | 50668 | spiced fries-onion | frozen food | 1 | 26.550 | 26.550 |
| 2 | 173131329 | 2022-11-21 00:02:36.017 | 5136784 | 2116 | banana african | fresh fruits | 1 | 24.273 | 24.273 |
| 3 | 173131329 | 2022-11-21 00:02:36.017 | 5136784 | 73732 | shredded mozzarela cheese | cheese | 2 | 37.800 | 75.600 |
| 4 | 173131329 | 2022-11-21 00:02:36.017 | 5136784 | 298472 | chicken drumsticks | frozen food | 1 | 28.575 | 28.575 |
df2 = df1[df1['Item_price'] >= 0]
#Converting Timestamp column to date-time format for time-intelligence Operation.
df2['Timestamp'] = pd.to_datetime(df2['Timestamp'])
C:\Users\sa2259\AppData\Local\Temp\ipykernel_32820\2581994025.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df2['Timestamp'] = pd.to_datetime(df2['Timestamp'])
#Adjusting From UTC to Regional Time
df2['Timestamp'] = df2['Timestamp'] + pd.Timedelta(hours=3)
C:\Users\sa2259\AppData\Local\Temp\ipykernel_32820\467794843.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df2['Timestamp'] = df2['Timestamp'] + pd.Timedelta(hours=3)
We are going to segregate Fresh and Non-fresh subdepartmens to better identify the choice taken by members, As most frequent buyers will have Fresh items dominantly in their top items while other items being present they will be overshadowed compared to these.
df2.head()
| Trx_id | Timestamp | Member_id | Item_id | Item_subclass | Item_subdepartment | Itm_qty | Item_price | Total_price | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 173131329 | 2022-11-21 03:02:36.017 | 5136784 | 1260 | tomato | fresh vegetable | 1 | 10.665 | 10.665 |
| 1 | 173131329 | 2022-11-21 03:02:36.017 | 5136784 | 50668 | spiced fries-onion | frozen food | 1 | 26.550 | 26.550 |
| 2 | 173131329 | 2022-11-21 03:02:36.017 | 5136784 | 2116 | banana african | fresh fruits | 1 | 24.273 | 24.273 |
| 3 | 173131329 | 2022-11-21 03:02:36.017 | 5136784 | 73732 | shredded mozzarela cheese | cheese | 2 | 37.800 | 75.600 |
| 4 | 173131329 | 2022-11-21 03:02:36.017 | 5136784 | 298472 | chicken drumsticks | frozen food | 1 | 28.575 | 28.575 |
# Extract time of day and create corresponding columns
def get_time_of_day(hour):
if 5 <= hour < 8:
return 'early_morning'
elif 8 <= hour < 12:
return 'morning'
elif 12 <= hour < 17:
return 'noon'
elif 17 <= hour < 20:
return 'evening'
elif 20 <= hour < 24:
return 'night'
else:
return 'late_night'
df2['hour'] = df2['Timestamp'].dt.hour
df2['time_of_day'] = df2['hour'].apply(get_time_of_day)
C:\Users\sa2259\AppData\Local\Temp\ipykernel_32820\3479529261.py:16: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df2['hour'] = df2['Timestamp'].dt.hour C:\Users\sa2259\AppData\Local\Temp\ipykernel_32820\3479529261.py:17: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df2['time_of_day'] = df2['hour'].apply(get_time_of_day)
df2.head()
| Trx_id | Timestamp | Member_id | Item_id | Item_subclass | Item_subdepartment | Itm_qty | Item_price | Total_price | hour | time_of_day | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 173131329 | 2022-11-21 03:02:36.017 | 5136784 | 1260 | tomato | fresh vegetable | 1 | 10.665 | 10.665 | 3 | late_night |
| 1 | 173131329 | 2022-11-21 03:02:36.017 | 5136784 | 50668 | spiced fries-onion | frozen food | 1 | 26.550 | 26.550 | 3 | late_night |
| 2 | 173131329 | 2022-11-21 03:02:36.017 | 5136784 | 2116 | banana african | fresh fruits | 1 | 24.273 | 24.273 | 3 | late_night |
| 3 | 173131329 | 2022-11-21 03:02:36.017 | 5136784 | 73732 | shredded mozzarela cheese | cheese | 2 | 37.800 | 75.600 | 3 | late_night |
| 4 | 173131329 | 2022-11-21 03:02:36.017 | 5136784 | 298472 | chicken drumsticks | frozen food | 1 | 28.575 | 28.575 | 3 | late_night |
agg_by_time = df2.groupby(
['Member_id', 'Item_id', 'Item_subclass', 'Item_subdepartment', 'time_of_day']
)[['Itm_qty', 'Item_price', 'Total_price']].sum().reset_index()
agg_by_time
| Member_id | Item_id | Item_subclass | Item_subdepartment | time_of_day | Itm_qty | Item_price | Total_price | |
|---|---|---|---|---|---|---|---|---|
| 0 | 4 | 1164 | banana | fresh fruits | evening | 2 | 12.447 | 12.447 |
| 1 | 4 | 1996 | zero vat | zero vat items | evening | 1 | 180.000 | 180.000 |
| 2 | 4 | 2192 | laban full fat | laban & labneh | noon | 1 | 1.800 | 1.800 |
| 3 | 4 | 3200 | fresh milk full fat | milk | evening | 1 | 5.400 | 5.400 |
| 4 | 4 | 4276 | dates | dates (supplier) | late_night | 1 | 20.250 | 20.250 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4510843 | 10723344 | 957260 | camel milk | milk | late_night | 1 | 11.250 | 11.250 |
| 4510844 | 10723414 | 88 | f bread | bakery | late_night | 4 | 4.140 | 16.560 |
| 4510845 | 10723414 | 49800 | fresh juice normal | fresh juice | late_night | 1 | 6.750 | 6.750 |
| 4510846 | 10723414 | 74436 | pickle & olive -weight | pickle & olive | late_night | 1 | 5.076 | 5.076 |
| 4510847 | 10723414 | 452304 | fresh juice normal | fresh juice | late_night | 1 | 6.750 | 6.750 |
4510848 rows × 8 columns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Set style for seaborn
sns.set(style="whitegrid")
plt.figure(figsize=(12, 6))
# Plot histogram for 'Itm_qty'
plt.subplot(1, 3, 1)
sns.histplot(agg_by_time['Itm_qty'], bins=20, kde=True)
plt.title('Distribution of Item Quantity')
# Plot histogram for 'Item_price'
plt.subplot(1, 3, 2)
sns.histplot(agg_by_time['Item_price'], bins=20, kde=True)
plt.title('Distribution of Item Price')
# Plot histogram for 'Total_price'
plt.subplot(1, 3, 3)
sns.histplot(agg_by_time['Total_price'], bins=20, kde=True)
plt.title('Distribution of Total Price')
plt.tight_layout()
plt.show()
plt.figure(figsize=(10, 8))
correlation_matrix = agg_by_time[['Itm_qty', 'Item_price', 'Total_price']].corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f')
plt.title('Correlation Matrix')
plt.show()
# Aggregate total prices by time of day
total_price_by_time = df2.groupby('time_of_day')['Total_price'].sum().reset_index()
# Create the bar chart
fig = px.bar(total_price_by_time, x='time_of_day', y='Total_price',
title='Total Sales Distribution by Time of Day',
labels={'Total_price': 'Total Price', 'time_of_day': 'Time of Day'},
color='time_of_day',
color_discrete_sequence=px.colors.sequential.Viridis)
# Customize the layout
fig.update_traces(texttemplate='%{y}', textposition='inside')
fig.update_layout(showlegend=False)
# Display the bar chart
fig.show()
# Aggregate total prices by time of day
total_price_by_time = df2.groupby('time_of_day')['Total_price'].sum().reset_index()
# Create the bar chart
fig = px.bar(total_price_by_time, x='time_of_day', y='Total_price',
title='Total Sales Distribution by Time of Day',
labels={'Total_price': 'Total Price', 'time_of_day': 'Time of Day'},
color='time_of_day',
color_discrete_sequence=px.colors.sequential.Viridis)
# Customize the layout with data labels formatted to 2 decimal places and larger font size
fig.update_traces(
texttemplate='%{y:.2f}', # Format labels to 2 decimal places
textposition='inside',
textfont=dict(size=14, color='White') # Increase font size and set color to black
)
fig.update_layout(showlegend=False)
# Display the bar chart
fig.show()
import pandas as pd
import plotly.express as px
# Load your dataset
# df2 = pd.read_csv('your_data.csv') # Uncomment and adjust as needed
# Group by 'Item_subdepartment' and calculate total sales
sales_per_item_subdepartment = df2.groupby('Item_subdepartment')['Total_price'].sum().reset_index()
# Create a bar plot using Plotly
fig = px.bar(
sales_per_item_subdepartment,
x='Item_subdepartment',
y='Total_price',
color='Total_price',
color_continuous_scale='viridis',
title='Total Sales by Item Subdepartment'
)
# Customize the layout
fig.update_layout(
xaxis_title='Item Subdepartment',
yaxis_title='Total Price',
xaxis_tickangle=-45
)
# Show the plot
fig.show()
import pandas as pd
import plotly.express as px
# Load your dataset
# df2 = pd.read_csv('your_data.csv') # Uncomment and adjust as needed
# Group by 'Item_subdepartment' and calculate total sales
sales_per_item_subdepartment = df2.groupby('Item_subdepartment')['Total_price'].sum().reset_index()
# Create a bar plot using Plotly
fig = px.bar(
sales_per_item_subdepartment,
x='Item_subdepartment',
y='Total_price',
color='Total_price',
color_continuous_scale='viridis',
title='Total Sales by Item Subdepartment',
text='Total_price' # Add data labels
)
# Customize the layout with data labels
fig.update_traces(
texttemplate='%{text:.2f}', # Format labels to 2 decimal places
textposition='outside', # Position labels outside the bars
textfont=dict(size=12, color='black') # Set font size and color
)
# Customize the layout
fig.update_layout(
xaxis_title='Item Subdepartment',
yaxis_title='Total Price',
xaxis_tickangle=-45
)
# Show the plot
fig.show()
import pandas as pd
import plotly.express as px
# Load your dataset
# df2 = pd.read_csv('your_data.csv') # Uncomment and adjust as needed
# Group by 'Item_subdepartment' and calculate total sales
sales_per_item_subclass = df2.groupby('Item_subclass')['Total_price'].sum().reset_index()
# Create a bar plot using Plotly
fig = px.bar(
sales_per_item_subclass,
x='Item_subclass',
y='Total_price',
color='Total_price',
color_continuous_scale='viridis',
title='Total Sales by Item Subclass'
)
# Customize the layout
fig.update_layout(
xaxis_title='Item Subclass',
yaxis_title='Total Price',
xaxis_tickangle=-45
)
# Show the plot
fig.show()
# Plot boxplots for 'Itm_qty', 'Item_price', and 'Total_price' to visualize outliers
plt.figure(figsize=(15, 5))
# Boxplot for 'Itm_qty'
plt.subplot(1, 3, 1)
sns.boxplot(data=df2, y='Itm_qty', palette='coolwarm')
plt.title('Boxplot of Item Quantity')
# Boxplot for 'Item_price'
plt.subplot(1, 3, 2)
sns.boxplot(data=df2, y='Item_price', palette='coolwarm')
plt.title('Boxplot of Item Price')
# Boxplot for 'Total_price'
plt.subplot(1, 3, 3)
sns.boxplot(data=df2, y='Total_price', palette='coolwarm')
plt.title('Boxplot of Total Price')
plt.tight_layout()
plt.show()
import pandas as pd
# Assuming df2 is your DataFrame
# 1. Get the top 100 products by total sales
top_100_products = df2.groupby('Item_id')['Total_price'].sum().reset_index()
top_100_products = top_100_products.sort_values(by='Total_price', ascending=False).head(100)
# 2. Get the top 10 subclasses by total sales
top_10_subclasses = df2.groupby('Item_subclass')['Total_price'].sum().reset_index()
top_10_subclasses = top_10_subclasses.sort_values(by='Total_price', ascending=False).head(10)
# 3. Get the top 10 subdepartments by total sales
top_10_subdepartments = df2.groupby('Item_subdepartment')['Total_price'].sum().reset_index()
top_10_subdepartments = top_10_subdepartments.sort_values(by='Total_price', ascending=False).head(10)
# Display the results
print("Top 100 Products by Sales:")
print(top_100_products)
print("\nTop 10 Subclasses by Sales:")
print(top_10_subclasses)
print("\nTop 10 Subdepartments by Sales:")
print(top_10_subdepartments)
Top 100 Products by Sales:
Item_id Total_price
81 1264 870351.939
424 3504 757484.055
5 28 622401.849
1429 9856 557529.939
148 1560 536595.804
... ... ...
3600 26812 110926.638
124 1452 109881.774
36785 730812 109510.875
10770 86520 109500.345
4622 37136 109489.041
[100 rows x 2 columns]
Top 10 Subclasses by Sales:
Item_subclass Total_price
580 fresh chicken 7535658.186
579 fresh beef 2856952.584
900 mineral water 2609127.405
237 cheese -weight 2554750.197
1332 supplier finished goods 2107845.837
212 carbonated drinks 2088466.236
864 managish 1928468.745
529 f cakes 1887850.359
584 fresh fish 1648223.739
599 fresh mutton 1595273.967
Top 10 Subdepartments by Sales:
Item_subdepartment Total_price
49 confectionary 1.007549e+07
103 frozen food 8.421967e+06
138 juices & beverages 7.721683e+06
95 fresh chicken 7.535658e+06
33 cheese 6.260865e+06
102 fresh vegetable 6.180319e+06
97 fresh fruits 5.174071e+06
15 bakery f-goods (in-house) 3.875085e+06
121 hot beverages 3.189698e+06
13 bakery 3.180744e+06
import pandas as pd
import plotly.express as px
# Assuming df2 is your DataFrame and has columns 'Member_id' and 'Total_price'
# 1. Calculate total revenue per member
revenue_per_member = df2.groupby('Member_id')['Total_price'].sum().reset_index()
# 2. Define revenue bands (buckets)
bins = [0, 100, 500, 1000, 5000, 10000, float('inf')] # Define your revenue bands
labels = ['0-100', '101-500', '501-1000', '1001-5000', '5001-10000', '10000+'] # Labels for each bucket
# Create a new column 'Revenue_Band' in the DataFrame
revenue_per_member['Revenue_Band'] = pd.cut(revenue_per_member['Total_price'], bins=bins, labels=labels)
# 3. Aggregate counts for each revenue band
revenue_band_counts = revenue_per_member['Revenue_Band'].value_counts().reset_index()
revenue_band_counts.columns = ['Revenue_Band', 'Count']
# Sort by descending order of count
revenue_band_counts = revenue_band_counts.sort_values(by='Count', ascending=False)
# 4. Plot a histogram of revenue bands
fig = px.bar(
revenue_band_counts,
x='Revenue_Band',
y='Count',
title='Histogram of Revenue Bands for Members',
labels={'Revenue_Band': 'Revenue Band', 'Count': 'Number of Members'},
color='Revenue_Band',
color_discrete_sequence=px.colors.sequential.Viridis
)
# Customize the layout
fig.update_layout(
xaxis_title='Revenue Band',
yaxis_title='Number of Members',
xaxis_tickangle=-45
)
# Show the plot
fig.show()
pattern = r'\bfresh\b'
# Find unique 'Product_subclass' values matching the pattern (case-insensitive)
df2[df2['Item_subdepartment'].str.contains(pattern, case=False, regex=True)]
| Trx_id | Timestamp | Member_id | Item_id | Item_subclass | Item_subdepartment | Itm_qty | Item_price | Total_price | hour | time_of_day | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 173131329 | 2022-11-21 03:02:36.017 | 5136784 | 1260 | tomato | fresh vegetable | 1 | 10.665 | 10.665 | 3 | late_night |
| 2 | 173131329 | 2022-11-21 03:02:36.017 | 5136784 | 2116 | banana african | fresh fruits | 1 | 24.273 | 24.273 | 3 | late_night |
| 8 | 173131329 | 2022-11-21 03:02:36.017 | 5136784 | 3860 | carrot | fresh vegetable | 1 | 1.926 | 1.926 | 3 | late_night |
| 15 | 173131344 | 2022-11-21 03:03:08.410 | 294748 | 1264 | mixed leaves | fresh vegetable | 1 | 1.350 | 1.350 | 3 | late_night |
| 23 | 173131329 | 2022-11-21 03:02:36.017 | 5136784 | 44 | onion african | fresh vegetable | 1 | 5.418 | 5.418 | 3 | late_night |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5500281 | 262036500 | 2023-07-21 02:20:50.880 | 2442722 | 156 | potato | fresh vegetable | 1 | 12.465 | 12.465 | 2 | late_night |
| 5500282 | 262036500 | 2023-07-21 02:20:50.880 | 2442722 | 2804 | fresh juice normal | fresh juice | 1 | 14.355 | 14.355 | 2 | late_night |
| 5500285 | 262036500 | 2023-07-21 02:20:50.880 | 2442722 | 41428 | grapes | fresh fruits | 2 | 10.710 | 21.420 | 2 | late_night |
| 5500287 | 262036500 | 2023-07-21 02:20:50.880 | 2442722 | 151976 | local cucumber | fresh vegetable | 1 | 8.955 | 8.955 | 2 | late_night |
| 5500288 | 262036500 | 2023-07-21 02:20:50.880 | 2442722 | 811944 | fresh juice normal | fresh juice | 3 | 5.400 | 16.200 | 2 | late_night |
1311747 rows × 11 columns
current_date = df2['Timestamp'].max()
# Compute additional features
member_features_df = df2.groupby('Member_id').agg(
Total_spend=('Total_price', 'sum'),
Total_trx=('Trx_id', 'nunique'),
Total_item=('Item_id', 'count'),
Total_unique_items=('Item_id','nunique'),
Total_unique_subclasses=('Item_subclass','nunique'),
Total_unique_subdepartments=('Item_subdepartment','nunique'),
Min_spend=('Total_price', 'min'),
Max_spend=('Total_price','max'),
days_since_last_purchase=('Timestamp', lambda x: (current_date - x.max()).days),
days_since_first_purchase=('Timestamp', lambda x: (current_date - x.min()).days),
).reset_index()
member_features_df['spend_per_trx'] = member_features_df['Total_spend'] / member_features_df['Total_trx']
member_features_df['Items_per_trx'] = np.ceil(member_features_df['Total_item'] / member_features_df['Total_trx'])
member_features_df['Unique_items_per_trx'] = np.ceil(member_features_df['Total_unique_items'] / member_features_df['Total_trx'])
periods = [7, 15, 60, 90]
# Most Frequently Purchased Products - FRESH (Top 5)
pattern_fresh = r'\bfresh\b'
def get_top_fresh_products_for_periods(df, periods, pattern_fresh):
# Calculate the maximum timestamp for each member
max_timestamp = df.groupby('Member_id')['Timestamp'].max().reset_index()
max_timestamp.columns = ['Member_id', 'Max_Timestamp']
# Merge the max_timestamp DataFrame with the original df to filter data
df_with_max = pd.merge(df, max_timestamp, on='Member_id')
# Initialize a list to store results for each period
results = []
for days in periods:
# Calculate the start date for the period relative to each member's maximum timestamp
df_with_max['Start_Date'] = df_with_max['Max_Timestamp'] - pd.Timedelta(days=days)
# Filter to include only the data for the specified period
filtered_data = df_with_max[(df_with_max['Timestamp'] >= df_with_max['Start_Date']) &
(df_with_max['Timestamp'] <= df_with_max['Max_Timestamp'])]
# Find most frequently purchased fresh products
product_freq_fresh = filtered_data[filtered_data['Item_subdepartment'].str.contains(pattern_fresh, case=False, regex=True)]
product_freq_fresh = product_freq_fresh.groupby(['Member_id', 'Item_id']).size().reset_index(name='counts')
product_freq_fresh['rank'] = product_freq_fresh.groupby('Member_id')['counts'].rank(method='first', ascending=False)
# Get the top products based on rank
top_products = product_freq_fresh[product_freq_fresh['rank'] <= 5]
# Pivot table to get top products for each member
top_products_pivot = top_products.pivot_table(
index='Member_id',
columns='rank',
values='Item_id',
aggfunc=lambda x: ', '.join(map(str, x)) # Aggregate product IDs as comma-separated strings
).reset_index()
# Rename columns
top_products_pivot.columns = [
'Member_id'
] + [f'top_{int(col)}_{days}_day_fresh_product' for col in top_products_pivot.columns if col != 'Member_id']
# Append the result to the list
results.append(top_products_pivot)
# Merge all results on 'Member_id'
combined_results = results[0]
for result in results[1:]:
combined_results = pd.merge(combined_results, result, on='Member_id', how='outer')
# Ensure no duplicate 'Member_id' columns
combined_results = combined_results.loc[:, ~combined_results.columns.duplicated()]
return combined_results
# Most Frequently Purchased Products - NON-FRESH (Top 5)
def get_top_non_fresh_products_for_periods(df, periods, pattern_fresh):
# Calculate the maximum timestamp for each member
max_timestamp = df.groupby('Member_id')['Timestamp'].max().reset_index()
max_timestamp.columns = ['Member_id', 'Max_Timestamp']
# Merge the max_timestamp DataFrame with the original df to filter data
df_with_max = pd.merge(df, max_timestamp, on='Member_id')
# Initialize a list to store results for each period
results = []
for days in periods:
# Calculate the start date for the period relative to each member's maximum timestamp
df_with_max['Start_Date'] = df_with_max['Max_Timestamp'] - pd.Timedelta(days=days)
# Filter to include only the data for the specified period
filtered_data = df_with_max[(df_with_max['Timestamp'] >= df_with_max['Start_Date']) &
(df_with_max['Timestamp'] <= df_with_max['Max_Timestamp'])]
# Find most frequently purchased non-fresh products
product_freq_non_fresh = filtered_data[~filtered_data['Item_subdepartment'].str.contains(pattern_fresh, case=False, regex=True)]
product_freq_non_fresh = product_freq_non_fresh.groupby(['Member_id', 'Item_id']).size().reset_index(name='counts')
product_freq_non_fresh['rank'] = product_freq_non_fresh.groupby('Member_id')['counts'].rank(method='first', ascending=False)
# Get the top products based on rank
top_products = product_freq_non_fresh[product_freq_non_fresh['rank'] <= 5]
# Pivot table to get top products for each member
top_products_pivot = top_products.pivot_table(
index='Member_id',
columns='rank',
values='Item_id',
aggfunc=lambda x: ', '.join(map(str, x)) # Aggregate product IDs as comma-separated strings
).reset_index()
# Rename columns
top_products_pivot.columns = [
'Member_id'
] + [f'top_{int(col)}_{days}_day_non_fresh_product' for col in top_products_pivot.columns if col != 'Member_id']
# Append the result to the list
results.append(top_products_pivot)
# Merge all results on 'Member_id'
combined_results = results[0]
for result in results[1:]:
combined_results = pd.merge(combined_results, result, on='Member_id', how='outer')
# Ensure no duplicate 'Member_id' columns
combined_results = combined_results.loc[:, ~combined_results.columns.duplicated()]
return combined_results
# Combine all features
top_5_products_fresh_pivot = get_top_fresh_products_for_periods(df2, periods, pattern_fresh)
member_features_df = member_features_df.merge(top_5_products_fresh_pivot, on='Member_id', how='left')
top_5_products_non_fresh_pivot = get_top_non_fresh_products_for_periods(df2, periods, pattern_fresh)
member_features_df = member_features_df.merge(top_5_products_non_fresh_pivot, on='Member_id', how='left')
# Compute additional features
item_features = df2.groupby('Item_id').agg(
total_purchases=('Item_id', 'count'),
total_spend=('Total_price', 'sum'),
total_Itm_qty_purchased=('Itm_qty', 'sum')
).reset_index()
item_features.head()
periods = [7, 15, 60, 90]
Max_timestamp_all = df2['Timestamp'].max()
results = []
def rename_columns(df, period):
columns = {}
for col in df.columns:
if col not in ['Item_id', 'Item_name', 'Item_subclass', 'Item_subdepartment']:
columns[col] = f"{col}_{period}_days"
df.rename(columns=columns, inplace=True)
return df
for days in periods:
# Calculate the start date for the period relative to each member's maximum timestamp
df2['Start_Date'] = Max_timestamp_all - pd.Timedelta(days=days)
# Filter to include only the data for the specified period
filtered_data = df2[(df2['Timestamp'] >= df2['Start_Date']) &
(df2['Timestamp'] <= Max_timestamp_all)]
# Find most frequently purchased fresh products
Trx_count_items = filtered_data.groupby('Item_id')['Trx_id'].count().reset_index()
Trx_count_items.columns = ['Item_id', 'Trx_count'] # Rename column for clarity
item_sales = filtered_data.groupby(['Item_id', 'Item_subclass', 'Item_subdepartment'])[['Total_price','Itm_qty']].sum().reset_index()
# Merge item_sales with Trx_count_items on 'Item_id'
item_sales = item_sales.merge(Trx_count_items, on='Item_id', how='outer')
# Rename columns with appropriate suffix
item_sales = rename_columns(item_sales, days)
# Append the result to the list
results.append(item_sales)
# Merge all period dataframes into one dataframe
final_df = results[0]
for i in range(1, len(results)):
final_df = final_df.merge(results[i], on=['Item_id', 'Item_subclass', 'Item_subdepartment'], how='outer')
# Fill NaN values with 0 if necessary
final_df.fillna(0, inplace=True)
C:\Users\sa2259\AppData\Local\Temp\ipykernel_32820\3092105094.py:25: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy C:\Users\sa2259\AppData\Local\Temp\ipykernel_32820\3092105094.py:25: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy C:\Users\sa2259\AppData\Local\Temp\ipykernel_32820\3092105094.py:25: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy C:\Users\sa2259\AppData\Local\Temp\ipykernel_32820\3092105094.py:25: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
pd.set_option('display.max_columns',None)
final_df.head()
| Item_id | Item_subclass | Item_subdepartment | Total_price_7_days | Itm_qty_7_days | Trx_count_7_days | Total_price_15_days | Itm_qty_15_days | Trx_count_15_days | Total_price_60_days | Itm_qty_60_days | Trx_count_60_days | Total_price_90_days | Itm_qty_90_days | Trx_count_90_days | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 16 | bar chocolates | confectionary | 13.275 | 3.0 | 2.0 | 21.240 | 6.0 | 3.0 | 162.315 | 37.0 | 21.0 | 291.915 | 75 | 46 |
| 1 | 20 | fresh milk full fat | milk | 3808.800 | 220.0 | 151.0 | 6950.952 | 422.0 | 330.0 | 22402.602 | 1411.0 | 1219.0 | 34131.645 | 2183 | 1920 |
| 2 | 24 | mineral water | juices & beverages | 7661.142 | 233.0 | 79.0 | 11367.702 | 442.0 | 173.0 | 28899.576 | 1792.0 | 717.0 | 43365.285 | 2838 | 1201 |
| 3 | 28 | zam zam water | juices & beverages | 14407.407 | 872.0 | 327.0 | 32636.610 | 1917.0 | 680.0 | 121929.507 | 7527.0 | 2663.0 | 190673.649 | 11794 | 4141 |
| 4 | 32 | mineral water | juices & beverages | 757.026 | 71.0 | 51.0 | 2714.328 | 275.0 | 154.0 | 11356.659 | 1044.0 | 509.0 | 14225.760 | 1351 | 664 |
Item_features_df = final_df.copy()
member_features_df.head()
| Member_id | Total_spend | Total_trx | Total_item | Total_unique_items | Total_unique_subclasses | Total_unique_subdepartments | Min_spend | Max_spend | days_since_last_purchase | days_since_first_purchase | spend_per_trx | Items_per_trx | Unique_items_per_trx | top_1_7_day_fresh_product | top_2_7_day_fresh_product | top_3_7_day_fresh_product | top_4_7_day_fresh_product | top_5_7_day_fresh_product | top_1_15_day_fresh_product | top_2_15_day_fresh_product | top_3_15_day_fresh_product | top_4_15_day_fresh_product | top_5_15_day_fresh_product | top_1_60_day_fresh_product | top_2_60_day_fresh_product | top_3_60_day_fresh_product | top_4_60_day_fresh_product | top_5_60_day_fresh_product | top_1_90_day_fresh_product | top_2_90_day_fresh_product | top_3_90_day_fresh_product | top_4_90_day_fresh_product | top_5_90_day_fresh_product | top_1_7_day_non_fresh_product | top_2_7_day_non_fresh_product | top_3_7_day_non_fresh_product | top_4_7_day_non_fresh_product | top_5_7_day_non_fresh_product | top_1_15_day_non_fresh_product | top_2_15_day_non_fresh_product | top_3_15_day_non_fresh_product | top_4_15_day_non_fresh_product | top_5_15_day_non_fresh_product | top_1_60_day_non_fresh_product | top_2_60_day_non_fresh_product | top_3_60_day_non_fresh_product | top_4_60_day_non_fresh_product | top_5_60_day_non_fresh_product | top_1_90_day_non_fresh_product | top_2_90_day_non_fresh_product | top_3_90_day_non_fresh_product | top_4_90_day_non_fresh_product | top_5_90_day_non_fresh_product | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 4 | 1168.092 | 18 | 40 | 30 | 22 | 17 | 1.80 | 305.100 | 38 | 225 | 64.894000 | 3.0 | 2.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1164 | NaN | NaN | NaN | NaN | 1164 | 59128 | 70912 | NaN | NaN | 767476 | NaN | NaN | NaN | NaN | 767476 | NaN | NaN | NaN | NaN | 3200 | 8748 | 22420 | 36444 | 44468 | 48848 | 1996 | 3200 | 8748 | 22420 |
| 1 | 6 | 1153.674 | 138 | 184 | 61 | 36 | 21 | 0.00 | 55.755 | 0 | 241 | 8.359957 | 2.0 | 1.0 | NaN | NaN | NaN | NaN | NaN | 156 | 48012 | NaN | NaN | NaN | 48012 | 156 | 1260 | 1264 | 2120 | 48012 | 156 | 44 | 1260 | 1264 | 1352 | 65428 | 66348 | 66460 | 66476 | 1352 | 65428 | 293556 | 2508 | 65436 | 1352 | 65436 | 68276 | 1356 | 1424 | 1352 | 65436 | 68276 | 1356 | 1424 |
| 2 | 12 | 8002.494 | 571 | 969 | 218 | 101 | 41 | 0.00 | 272.880 | 0 | 241 | 14.014876 | 2.0 | 1.0 | 44 | 176 | 1260 | 1264 | 2316 | 44 | 176 | 1260 | 1264 | 2316 | 44 | 176 | 1260 | 1264 | 2316 | 44 | 176 | 1260 | 1264 | 2316 | 1352 | 65436 | 86780 | 2624 | 65452 | 1352 | 65436 | 65452 | 2624 | 1424 | 1352 | 65436 | 65452 | 1424 | 1500 | 1352 | 65436 | 65452 | 1424 | 1500 |
| 3 | 14 | 985.077 | 32 | 92 | 69 | 49 | 26 | 0.00 | 46.755 | 20 | 237 | 30.783656 | 3.0 | 3.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 8764 | 12356 | 14468 | 67116 | 88740 | 8764 | 12356 | 14468 | 67116 | 88740 | 1356 | NaN | NaN | NaN | NaN | 1356 | NaN | NaN | NaN | NaN | 1356 | 8408 | 10196 | 16176 | 30340 | 1356 | 66448 | 1500 | 3428 | 8408 |
| 4 | 18 | 1080.333 | 18 | 37 | 25 | 17 | 10 | 1.35 | 445.500 | 3 | 233 | 60.018500 | 3.0 | 2.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1504 | NaN | NaN | NaN | NaN | 1352 | 2492 | NaN | NaN | NaN | 1352 | 2492 | NaN | NaN | NaN | 1352 | 2492 | 12780 | 51584 | 1356 | 1352 | 2492 | 1356 | 12780 | 51584 |
member_features_df = member_features_df.sort_values(by='Total_trx', ascending=False).head(1000)
member_features_df.fillna('NA',inplace=True)
Item_features_df.fillna('NA',inplace=True)
#Checking for nulls in both datasets.
print(f'Members_dataset:\n{member_features_df.isna().sum().sum()}\n')
print(f'Items_dataset:\n{Item_features_df.isna().sum().sum()}')
Members_dataset: 0 Items_dataset: 0
numerical_columns_members = np.array(member_features_df.columns[1:14])
# Get columns from index 58 onward
categorical_columns_from_58 = member_features_df.columns[14:]
# Get column 0
categorical_column_0 = member_features_df.columns[0]
# Combine both lists of columns
categorical_columns_members = np.concatenate(([categorical_column_0], categorical_columns_from_58))
categorical_columns_items = np.array(Item_features_df.columns[:3])
numerical_columns_items = np.array(Item_features_df.columns[3:])
# Define categorical and numerical features
categorical_features = {
"members": categorical_columns_members, # Example user features
"items": categorical_columns_items # Example item features
}
numerical_features = {
"members": numerical_columns_members, # Example user numerical features
"items": numerical_columns_items # Example item numerical features
}
# Create dictionary of unique features
unique_features_members_categorical = {}
unique_features_members_numerical = {}
unique_features_items_categorical = {}
unique_features_items_numerical = {}
# Helper function to convert values to strings
def convert_to_strings(values):
return [str(value) for value in values]
def convert_to_float(values):
return [float(value) for value in values]
# Extract unique values for member features
for feature in categorical_features["members"]:
unique_values = member_features_df[feature].unique().tolist()
unique_features_members_categorical[feature] = convert_to_strings(unique_values)
for feature in numerical_features["members"]:
unique_values = member_features_df[feature].unique().tolist()
unique_features_members_numerical[feature] = convert_to_float(unique_values)
# Extract unique values for item features
for feature in categorical_features["items"]:
unique_values = Item_features_df[feature].unique().tolist()
unique_features_items_categorical[feature] = convert_to_strings(unique_values)
for feature in numerical_features["items"]:
unique_values = Item_features_df[feature].unique().tolist()
unique_features_items_numerical[feature] = convert_to_float(unique_values)
# Convert dictionaries of unique features to tensors
unique_features_members_categorical_tf = {
feature_name: tf.constant(vocabulary) for feature_name, vocabulary in unique_features_members_categorical.items()
}
unique_features_members_numerical_tf = {
feature_name: tf.constant(values) for feature_name, values in unique_features_members_numerical.items()
}
unique_features_items_categorical_tf = {
feature_name: tf.constant(vocabulary) for feature_name, vocabulary in unique_features_items_categorical.items()
}
unique_features_items_numerical_tf = {
feature_name: tf.constant(values) for feature_name, values in unique_features_items_numerical.items()
}
Unique_member_ids_tf = tf.constant(unique_features_members_categorical['Member_id'])
Unique_Item_ids_tf = tf.constant(unique_features_items_categorical['Item_id'])
# Compute correlation matrix
correlation_matrix = member_features_df[numerical_columns_members].corr()
# Plot heatmap
plt.figure(figsize=(12, 10))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=.5)
plt.title('Correlation Matrix of Numerical Features')
plt.show()
# Compute correlation matrix
correlation_matrix = Item_features_df[numerical_columns_items].corr()
# Plot heatmap
plt.figure(figsize=(12, 10))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=.5)
plt.title('Correlation Matrix of Numerical Features')
plt.show()
# Initialize the combined dictionary
unique_features_members_combined = {}
# Process categorical features
for feature in categorical_features["members"]:
unique_values = member_features_df[feature].unique().tolist()
unique_features_members_combined[feature] = convert_to_strings(unique_values)
# Process numerical features
for feature in numerical_features["members"]:
unique_values = member_features_df[feature].unique().tolist()
unique_features_members_combined[feature] = convert_to_float(unique_values)
# Initialize the combined dictionary
unique_features_items_combined = {}
# Process categorical features
for feature in categorical_features["items"]:
unique_values = Item_features_df[feature].unique().tolist()
unique_features_items_combined[feature] = convert_to_strings(unique_values)
# Process numerical features
for feature in numerical_features["items"]:
unique_values = Item_features_df[feature].unique().tolist()
unique_features_items_combined[feature] = convert_to_float(unique_values)
member_features = {feature_name: tf.constant(values) for feature_name, values in unique_features_members_combined.items()}
item_features = {feature_name: tf.constant(values) for feature_name, values in unique_features_items_combined.items()}
import tensorflow as tf
class MemberModel(tf.keras.Model):
def __init__(self, unique_member_ids, unique_features_categorical, unique_features_numerical, embedding_dim=32):
super().__init__()
self.embedding_dim = embedding_dim
# Initialize the embedding layers dictionary
self.embedding_layers = {}
# Initialize normalization layers
self.normalization_layers = {}
# Initialize the Member_id lookup and embedding layer
self.member_lookup = tf.keras.layers.StringLookup(vocabulary=unique_member_ids, mask_token=None, oov_token=None)
self.member_embedding_layer = tf.keras.layers.Embedding(input_dim=self.member_lookup.vocabulary_size(), output_dim=embedding_dim)
# Create embedding layers for other categorical features
for feature_name, vocabulary in unique_features_categorical.items():
if feature_name != 'Member_id':
lookup = tf.keras.layers.StringLookup(vocabulary=vocabulary, mask_token=None, oov_token=None)
vocab_size = lookup.vocabulary_size()
embedding = tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim)
self.embedding_layers[feature_name] = (lookup, embedding)
# Create normalization layers for numerical features
for feature_name in unique_features_numerical:
normalization_layer = tf.keras.layers.LayerNormalization(axis=-1, epsilon=1e-6)
self.normalization_layers[feature_name] = normalization_layer
def call(self, member_features):
# Extract member_id and convert to embeddings
member_id = member_features['Member_id']
member_id_indices = self.member_lookup(member_id)
member_embeddings = self.member_embedding_layer(member_id_indices)
print(f"Shape of member_embeddings: {member_embeddings.shape}")
# Prepare list for concatenation
concatenated_features = [member_embeddings]
# Process categorical features
for feature_name, feature_values in member_features.items():
if feature_name != 'Member_id':
if feature_name in self.embedding_layers:
lookup, embedding = self.embedding_layers[feature_name]
feature_indices = lookup(feature_values)
feature_embeddings = embedding(feature_indices)
print(f"Shape of {feature_name} feature_embeddings (before padding): {feature_embeddings.shape}")
# Pad if needed to match batch size of member_embeddings
if feature_embeddings.shape[0] < member_embeddings.shape[0]:
padding_size = member_embeddings.shape[0] - feature_embeddings.shape[0]
feature_embeddings = tf.pad(feature_embeddings, [[0, padding_size], [0, 0]], constant_values=0)
print(f"Shape of {feature_name} feature_embeddings (after padding): {feature_embeddings.shape}")
concatenated_features.append(feature_embeddings)
# Process numerical features
for feature_name, feature_values in member_features.items():
if feature_name in self.normalization_layers:
normalization_layer = self.normalization_layers[feature_name]
normalized_values = normalization_layer(tf.expand_dims(feature_values, axis=0))
normalized_values = tf.squeeze(normalized_values, axis=0)
print(f"Shape of {feature_name} normalized_values (before padding): {normalized_values.shape}")
# Pad if needed to match batch size
if normalized_values.shape[0] < member_embeddings.shape[0]:
padding_size = member_embeddings.shape[0] - normalized_values.shape[0]
normalized_values = tf.pad(normalized_values, [[0, padding_size]], constant_values=0)
print(f"Shape of {feature_name} normalized_values (after padding): {normalized_values.shape}")
concatenated_features.append(tf.expand_dims(normalized_values, axis=-1))
# Concatenate all tensors along the last axis
final_concatenated_tensor = tf.concat(concatenated_features, axis=-1)
print(f"Shape of final_concatenated_tensor: {final_concatenated_tensor.shape}")
return final_concatenated_tensor
member_model = MemberModel(
unique_member_ids=Unique_member_ids_tf,
unique_features_categorical=unique_features_members_categorical_tf,
unique_features_numerical=unique_features_members_numerical_tf,
embedding_dim=3
)
output_member_embedding = member_model(member_features)
Shape of member_embeddings: (1000, 3) Shape of top_1_7_day_fresh_product feature_embeddings (before padding): (140, 3) Shape of top_1_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_7_day_fresh_product feature_embeddings (before padding): (170, 3) Shape of top_2_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_7_day_fresh_product feature_embeddings (before padding): (160, 3) Shape of top_3_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_7_day_fresh_product feature_embeddings (before padding): (150, 3) Shape of top_4_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_7_day_fresh_product feature_embeddings (before padding): (152, 3) Shape of top_5_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_15_day_fresh_product feature_embeddings (before padding): (143, 3) Shape of top_1_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_15_day_fresh_product feature_embeddings (before padding): (168, 3) Shape of top_2_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_15_day_fresh_product feature_embeddings (before padding): (169, 3) Shape of top_3_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_15_day_fresh_product feature_embeddings (before padding): (166, 3) Shape of top_4_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_15_day_fresh_product feature_embeddings (before padding): (154, 3) Shape of top_5_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_60_day_fresh_product feature_embeddings (before padding): (147, 3) Shape of top_1_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_60_day_fresh_product feature_embeddings (before padding): (170, 3) Shape of top_2_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_60_day_fresh_product feature_embeddings (before padding): (177, 3) Shape of top_3_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_60_day_fresh_product feature_embeddings (before padding): (174, 3) Shape of top_4_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_60_day_fresh_product feature_embeddings (before padding): (185, 3) Shape of top_5_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_90_day_fresh_product feature_embeddings (before padding): (136, 3) Shape of top_1_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_90_day_fresh_product feature_embeddings (before padding): (174, 3) Shape of top_2_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_90_day_fresh_product feature_embeddings (before padding): (163, 3) Shape of top_3_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_90_day_fresh_product feature_embeddings (before padding): (182, 3) Shape of top_4_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_90_day_fresh_product feature_embeddings (before padding): (180, 3) Shape of top_5_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_7_day_non_fresh_product feature_embeddings (before padding): (402, 3) Shape of top_1_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_7_day_non_fresh_product feature_embeddings (before padding): (487, 3) Shape of top_2_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_7_day_non_fresh_product feature_embeddings (before padding): (531, 3) Shape of top_3_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_7_day_non_fresh_product feature_embeddings (before padding): (561, 3) Shape of top_4_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_7_day_non_fresh_product feature_embeddings (before padding): (583, 3) Shape of top_5_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_15_day_non_fresh_product feature_embeddings (before padding): (401, 3) Shape of top_1_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_15_day_non_fresh_product feature_embeddings (before padding): (487, 3) Shape of top_2_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_15_day_non_fresh_product feature_embeddings (before padding): (523, 3) Shape of top_3_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_15_day_non_fresh_product feature_embeddings (before padding): (560, 3) Shape of top_4_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_15_day_non_fresh_product feature_embeddings (before padding): (602, 3) Shape of top_5_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_60_day_non_fresh_product feature_embeddings (before padding): (366, 3) Shape of top_1_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_60_day_non_fresh_product feature_embeddings (before padding): (457, 3) Shape of top_2_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_60_day_non_fresh_product feature_embeddings (before padding): (515, 3) Shape of top_3_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_60_day_non_fresh_product feature_embeddings (before padding): (541, 3) Shape of top_4_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_60_day_non_fresh_product feature_embeddings (before padding): (585, 3) Shape of top_5_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_90_day_non_fresh_product feature_embeddings (before padding): (352, 3) Shape of top_1_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_90_day_non_fresh_product feature_embeddings (before padding): (432, 3) Shape of top_2_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_90_day_non_fresh_product feature_embeddings (before padding): (495, 3) Shape of top_3_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_90_day_non_fresh_product feature_embeddings (before padding): (535, 3) Shape of top_4_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_90_day_non_fresh_product feature_embeddings (before padding): (558, 3) Shape of top_5_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of Total_spend normalized_values (before padding): (999,) Shape of Total_spend normalized_values (after padding): (1000,) Shape of Total_trx normalized_values (before padding): (219,) Shape of Total_trx normalized_values (after padding): (1000,) Shape of Total_item normalized_values (before padding): (680,) Shape of Total_item normalized_values (after padding): (1000,) Shape of Total_unique_items normalized_values (before padding): (453,) Shape of Total_unique_items normalized_values (after padding): (1000,) Shape of Total_unique_subclasses normalized_values (before padding): (231,) Shape of Total_unique_subclasses normalized_values (after padding): (1000,) Shape of Total_unique_subdepartments normalized_values (before padding): (79,) Shape of Total_unique_subdepartments normalized_values (after padding): (1000,) Shape of Min_spend normalized_values (before padding): (92,) Shape of Min_spend normalized_values (after padding): (1000,) Shape of Max_spend normalized_values (before padding): (681,) Shape of Max_spend normalized_values (after padding): (1000,) Shape of days_since_last_purchase normalized_values (before padding): (74,) Shape of days_since_last_purchase normalized_values (after padding): (1000,) Shape of days_since_first_purchase normalized_values (before padding): (79,) Shape of days_since_first_purchase normalized_values (after padding): (1000,) Shape of spend_per_trx normalized_values (before padding): (1000,) Shape of Items_per_trx normalized_values (before padding): (21,) Shape of Items_per_trx normalized_values (after padding): (1000,) Shape of Unique_items_per_trx normalized_values (before padding): (11,) Shape of Unique_items_per_trx normalized_values (after padding): (1000,) Shape of final_concatenated_tensor: (1000, 136) Shape of member_embeddings: (1000, 3) Shape of top_1_7_day_fresh_product feature_embeddings (before padding): (140, 3) Shape of top_1_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_7_day_fresh_product feature_embeddings (before padding): (170, 3) Shape of top_2_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_7_day_fresh_product feature_embeddings (before padding): (160, 3) Shape of top_3_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_7_day_fresh_product feature_embeddings (before padding): (150, 3) Shape of top_4_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_7_day_fresh_product feature_embeddings (before padding): (152, 3) Shape of top_5_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_15_day_fresh_product feature_embeddings (before padding): (143, 3) Shape of top_1_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_15_day_fresh_product feature_embeddings (before padding): (168, 3) Shape of top_2_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_15_day_fresh_product feature_embeddings (before padding): (169, 3) Shape of top_3_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_15_day_fresh_product feature_embeddings (before padding): (166, 3) Shape of top_4_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_15_day_fresh_product feature_embeddings (before padding): (154, 3) Shape of top_5_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_60_day_fresh_product feature_embeddings (before padding): (147, 3) Shape of top_1_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_60_day_fresh_product feature_embeddings (before padding): (170, 3) Shape of top_2_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_60_day_fresh_product feature_embeddings (before padding): (177, 3) Shape of top_3_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_60_day_fresh_product feature_embeddings (before padding): (174, 3) Shape of top_4_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_60_day_fresh_product feature_embeddings (before padding): (185, 3) Shape of top_5_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_90_day_fresh_product feature_embeddings (before padding): (136, 3) Shape of top_1_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_90_day_fresh_product feature_embeddings (before padding): (174, 3) Shape of top_2_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_90_day_fresh_product feature_embeddings (before padding): (163, 3) Shape of top_3_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_90_day_fresh_product feature_embeddings (before padding): (182, 3) Shape of top_4_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_90_day_fresh_product feature_embeddings (before padding): (180, 3) Shape of top_5_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_7_day_non_fresh_product feature_embeddings (before padding): (402, 3) Shape of top_1_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_7_day_non_fresh_product feature_embeddings (before padding): (487, 3) Shape of top_2_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_7_day_non_fresh_product feature_embeddings (before padding): (531, 3) Shape of top_3_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_7_day_non_fresh_product feature_embeddings (before padding): (561, 3) Shape of top_4_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_7_day_non_fresh_product feature_embeddings (before padding): (583, 3) Shape of top_5_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_15_day_non_fresh_product feature_embeddings (before padding): (401, 3) Shape of top_1_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_15_day_non_fresh_product feature_embeddings (before padding): (487, 3) Shape of top_2_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_15_day_non_fresh_product feature_embeddings (before padding): (523, 3) Shape of top_3_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_15_day_non_fresh_product feature_embeddings (before padding): (560, 3) Shape of top_4_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_15_day_non_fresh_product feature_embeddings (before padding): (602, 3) Shape of top_5_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_60_day_non_fresh_product feature_embeddings (before padding): (366, 3) Shape of top_1_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_60_day_non_fresh_product feature_embeddings (before padding): (457, 3) Shape of top_2_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_60_day_non_fresh_product feature_embeddings (before padding): (515, 3) Shape of top_3_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_60_day_non_fresh_product feature_embeddings (before padding): (541, 3) Shape of top_4_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_60_day_non_fresh_product feature_embeddings (before padding): (585, 3) Shape of top_5_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_90_day_non_fresh_product feature_embeddings (before padding): (352, 3) Shape of top_1_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_90_day_non_fresh_product feature_embeddings (before padding): (432, 3) Shape of top_2_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_90_day_non_fresh_product feature_embeddings (before padding): (495, 3) Shape of top_3_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_90_day_non_fresh_product feature_embeddings (before padding): (535, 3) Shape of top_4_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_90_day_non_fresh_product feature_embeddings (before padding): (558, 3) Shape of top_5_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of Total_spend normalized_values (before padding): (999,) Shape of Total_spend normalized_values (after padding): (1000,) Shape of Total_trx normalized_values (before padding): (219,) Shape of Total_trx normalized_values (after padding): (1000,) Shape of Total_item normalized_values (before padding): (680,) Shape of Total_item normalized_values (after padding): (1000,) Shape of Total_unique_items normalized_values (before padding): (453,) Shape of Total_unique_items normalized_values (after padding): (1000,) Shape of Total_unique_subclasses normalized_values (before padding): (231,) Shape of Total_unique_subclasses normalized_values (after padding): (1000,) Shape of Total_unique_subdepartments normalized_values (before padding): (79,) Shape of Total_unique_subdepartments normalized_values (after padding): (1000,) Shape of Min_spend normalized_values (before padding): (92,) Shape of Min_spend normalized_values (after padding): (1000,) Shape of Max_spend normalized_values (before padding): (681,) Shape of Max_spend normalized_values (after padding): (1000,) Shape of days_since_last_purchase normalized_values (before padding): (74,) Shape of days_since_last_purchase normalized_values (after padding): (1000,) Shape of days_since_first_purchase normalized_values (before padding): (79,) Shape of days_since_first_purchase normalized_values (after padding): (1000,) Shape of spend_per_trx normalized_values (before padding): (1000,) Shape of Items_per_trx normalized_values (before padding): (21,) Shape of Items_per_trx normalized_values (after padding): (1000,) Shape of Unique_items_per_trx normalized_values (before padding): (11,) Shape of Unique_items_per_trx normalized_values (after padding): (1000,) Shape of final_concatenated_tensor: (1000, 136)
import tensorflow as tf
class ItemModel(tf.keras.Model):
def __init__(self, unique_Item_ids, unique_features_categorical, unique_features_numerical, embedding_dim=32):
super().__init__()
self.embedding_dim = embedding_dim
# Initialize the embedding layers dictionary
self.embedding_layers = {}
# Initialize normalization layers
self.normalization_layers = {}
# Initialize the Item_id lookup and embedding layer
self.Item_lookup = tf.keras.layers.StringLookup(vocabulary=unique_Item_ids, mask_token=None, oov_token=None)
self.Item_embedding_layer = tf.keras.layers.Embedding(input_dim=self.Item_lookup.vocabulary_size(), output_dim=embedding_dim)
# Create embedding layers for other categorical features
for feature_name, vocabulary in unique_features_categorical.items():
if feature_name != 'Item_id':
lookup = tf.keras.layers.StringLookup(vocabulary=vocabulary, mask_token=None, oov_token=None)
vocab_size = lookup.vocabulary_size()
embedding = tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim)
self.embedding_layers[feature_name] = (lookup, embedding)
# Create normalization layers for numerical features
for feature_name in unique_features_numerical:
normalization_layer = tf.keras.layers.LayerNormalization(axis=-1, epsilon=1e-6)
self.normalization_layers[feature_name] = normalization_layer
def call(self, Item_features):
# Extract Item_id and convert to embeddings
Item_id = Item_features['Item_id']
Item_id_indices = self.Item_lookup(Item_id)
Item_embeddings = self.Item_embedding_layer(Item_id_indices)
print(f"Shape of Item_embeddings: {Item_embeddings.shape}")
# Prepare list for concatenation
concatenated_features = [Item_embeddings]
# Process categorical features
for feature_name, feature_values in Item_features.items():
if feature_name != 'Item_id':
if feature_name in self.embedding_layers:
lookup, embedding = self.embedding_layers[feature_name]
feature_indices = lookup(feature_values)
feature_embeddings = embedding(feature_indices)
print(f"Shape of {feature_name} feature_embeddings (before padding): {feature_embeddings.shape}")
# Pad if needed to match batch size of Item_embeddings
if feature_embeddings.shape[0] < Item_embeddings.shape[0]:
padding_size = Item_embeddings.shape[0] - feature_embeddings.shape[0]
feature_embeddings = tf.pad(feature_embeddings, [[0, padding_size], [0, 0]], constant_values=0)
print(f"Shape of {feature_name} feature_embeddings (after padding): {feature_embeddings.shape}")
concatenated_features.append(feature_embeddings)
# Process numerical features
for feature_name, feature_values in Item_features.items():
if feature_name in self.normalization_layers:
normalization_layer = self.normalization_layers[feature_name]
normalized_values = normalization_layer(tf.expand_dims(feature_values, axis=0))
normalized_values = tf.squeeze(normalized_values, axis=0)
print(f"Shape of {feature_name} normalized_values (before padding): {normalized_values.shape}")
# Pad if needed to match batch size
if normalized_values.shape[0] < Item_embeddings.shape[0]:
padding_size = Item_embeddings.shape[0] - normalized_values.shape[0]
normalized_values = tf.pad(normalized_values, [[0, padding_size]], constant_values=0)
print(f"Shape of {feature_name} normalized_values (after padding): {normalized_values.shape}")
concatenated_features.append(tf.expand_dims(normalized_values, axis=-1))
# Concatenate all tensors along the last axis
final_concatenated_tensor = tf.concat(concatenated_features, axis=-1)
print(f"Shape of final_concatenated_tensor: {final_concatenated_tensor.shape}")
return final_concatenated_tensor
item_model = ItemModel(
unique_Item_ids=Unique_Item_ids_tf,
unique_features_categorical=unique_features_items_categorical_tf,
unique_features_numerical=unique_features_items_numerical_tf,
embedding_dim=3
)
output_item_embedding = item_model(item_features)
Shape of Item_embeddings: (34663, 3) Shape of Item_subclass feature_embeddings (before padding): (1374, 3) Shape of Item_subclass feature_embeddings (after padding): (34663, 3) Shape of Item_subdepartment feature_embeddings (before padding): (220, 3) Shape of Item_subdepartment feature_embeddings (after padding): (34663, 3) Shape of Total_price_7_days normalized_values (before padding): (4776,) Shape of Total_price_7_days normalized_values (after padding): (34663,) Shape of Itm_qty_7_days normalized_values (before padding): (272,) Shape of Itm_qty_7_days normalized_values (after padding): (34663,) Shape of Trx_count_7_days normalized_values (before padding): (217,) Shape of Trx_count_7_days normalized_values (after padding): (34663,) Shape of Total_price_15_days normalized_values (before padding): (7245,) Shape of Total_price_15_days normalized_values (after padding): (34663,) Shape of Itm_qty_15_days normalized_values (before padding): (434,) Shape of Itm_qty_15_days normalized_values (after padding): (34663,) Shape of Trx_count_15_days normalized_values (before padding): (346,) Shape of Trx_count_15_days normalized_values (after padding): (34663,) Shape of Total_price_60_days normalized_values (before padding): (13710,) Shape of Total_price_60_days normalized_values (after padding): (34663,) Shape of Itm_qty_60_days normalized_values (before padding): (951,) Shape of Itm_qty_60_days normalized_values (after padding): (34663,) Shape of Trx_count_60_days normalized_values (before padding): (738,) Shape of Trx_count_60_days normalized_values (after padding): (34663,) Shape of Total_price_90_days normalized_values (before padding): (16172,) Shape of Total_price_90_days normalized_values (after padding): (34663,) Shape of Itm_qty_90_days normalized_values (before padding): (1193,) Shape of Itm_qty_90_days normalized_values (after padding): (34663,) Shape of Trx_count_90_days normalized_values (before padding): (947,) Shape of Trx_count_90_days normalized_values (after padding): (34663,) Shape of final_concatenated_tensor: (34663, 21) Shape of Item_embeddings: (34663, 3) Shape of Item_subclass feature_embeddings (before padding): (1374, 3) Shape of Item_subclass feature_embeddings (after padding): (34663, 3) Shape of Item_subdepartment feature_embeddings (before padding): (220, 3) Shape of Item_subdepartment feature_embeddings (after padding): (34663, 3) Shape of Total_price_7_days normalized_values (before padding): (4776,) Shape of Total_price_7_days normalized_values (after padding): (34663,) Shape of Itm_qty_7_days normalized_values (before padding): (272,) Shape of Itm_qty_7_days normalized_values (after padding): (34663,) Shape of Trx_count_7_days normalized_values (before padding): (217,) Shape of Trx_count_7_days normalized_values (after padding): (34663,) Shape of Total_price_15_days normalized_values (before padding): (7245,) Shape of Total_price_15_days normalized_values (after padding): (34663,) Shape of Itm_qty_15_days normalized_values (before padding): (434,) Shape of Itm_qty_15_days normalized_values (after padding): (34663,) Shape of Trx_count_15_days normalized_values (before padding): (346,) Shape of Trx_count_15_days normalized_values (after padding): (34663,) Shape of Total_price_60_days normalized_values (before padding): (13710,) Shape of Total_price_60_days normalized_values (after padding): (34663,) Shape of Itm_qty_60_days normalized_values (before padding): (951,) Shape of Itm_qty_60_days normalized_values (after padding): (34663,) Shape of Trx_count_60_days normalized_values (before padding): (738,) Shape of Trx_count_60_days normalized_values (after padding): (34663,) Shape of Total_price_90_days normalized_values (before padding): (16172,) Shape of Total_price_90_days normalized_values (after padding): (34663,) Shape of Itm_qty_90_days normalized_values (before padding): (1193,) Shape of Itm_qty_90_days normalized_values (after padding): (34663,) Shape of Trx_count_90_days normalized_values (before padding): (947,) Shape of Trx_count_90_days normalized_values (after padding): (34663,) Shape of final_concatenated_tensor: (34663, 21)
member_features = {feature_name: tf.constant(values) for feature_name, values in unique_features_members_combined.items()}
#Print the result to verify
for feature_name, tensor in member_features.items():
print(f"Feature: {feature_name}")
print(f"Tensor: {tensor.numpy()}")
item_features = {feature_name: tf.constant(values) for feature_name, values in unique_features_items_combined.items()}
#Print the result to verify
for feature_name, tensor in member_features.items():
print(f"Feature: {feature_name}")
print(f"Tensor: {tensor.numpy()}")
Feature: Member_id
Tensor: [b'124334' b'6765190' b'12' b'3902052' b'168740' b'6450644' b'4562'
b'51972' b'284' b'44238' b'216' b'8739954' b'39274' b'42198' b'12956'
b'106868' b'53198' b'13060' b'39882' b'40594' b'44074' b'170' b'4147466'
b'33606' b'1343564' b'108' b'258' b'39812' b'43342' b'9613730' b'13036'
b'50366' b'2208732' b'39926' b'8106' b'7990616' b'2961522' b'38554'
b'154' b'95314' b'38828' b'3215042' b'28440' b'42324' b'42136' b'39774'
b'5156212' b'2484' b'4160930' b'12946' b'44338' b'3861806' b'13056'
b'282' b'5787444' b'46616' b'42342' b'7918' b'49190' b'108728' b'146986'
b'1159308' b'1228848' b'12958' b'2806864' b'43306' b'12600' b'88'
b'40206' b'8180372' b'7545546' b'1299470' b'40856' b'149206' b'48784'
b'44146' b'10862' b'6299724' b'42190' b'127076' b'3212' b'12908'
b'2494898' b'6124522' b'862986' b'4244724' b'330' b'42760' b'9090622'
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b'267292' b'10888' b'40798' b'651944' b'41174' b'2200558' b'10502' b'134'
b'7894442' b'416692' b'2988' b'5944354' b'143050' b'1983758' b'45340'
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b'7443562' b'325464' b'12902' b'171650' b'9314264' b'256' b'154962'
b'702968' b'414812' b'296' b'1624616' b'42188' b'5218530' b'2305294'
b'28824' b'292' b'39848' b'38544' b'91500' b'47354' b'4109950' b'44286'
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b'13080' b'6062346' b'8927630' b'4539922' b'5337916' b'124264' b'48384'
b'43850' b'5685516' b'408250' b'47512' b'78716' b'2850464' b'42302'
b'13016' b'9160' b'1988138' b'86' b'299996' b'2479546' b'40456' b'654654'
b'11300' b'41508' b'516892' b'174644' b'406980' b'156702' b'83996'
b'13004' b'2200870' b'13062' b'8188696' b'527260' b'2867512' b'96718'
b'6324' b'3961372' b'7396626' b'2262208' b'40656' b'119380' b'9127496'
b'1277310' b'389828' b'5142878' b'2802666' b'6522868' b'157318' b'184562'
b'105034' b'118' b'300694' b'6768022' b'12968' b'6260' b'541894'
b'4999036' b'627212' b'22892' b'2543574' b'4678236' b'43712' b'2258044'
b'6417010' b'1241626' b'1023626' b'4606' b'136310' b'41758' b'375586'
b'45766' b'7724514' b'140' b'9204346' b'111212' b'1568220' b'41826'
b'40580' b'83766' b'2484116' b'110' b'3851122' b'13438' b'528348'
b'142590' b'905350' b'1078994' b'12990' b'1629674' b'363210' b'40854'
b'44128' b'244' b'9620590' b'44560' b'2781138' b'114558' b'1719104'
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b'6610964' b'380988' b'384' b'391784' b'189070' b'9175152' b'45154'
b'1194310' b'34908' b'78768' b'5574' b'31288' b'6824350' b'39834'
b'44612' b'46598' b'2184766' b'578904' b'34720' b'387216' b'44680'
b'39806' b'13436' b'6' b'1011566' b'1055992' b'335404' b'42488' b'8384'
b'13552' b'3304044' b'12874' b'686832' b'314492' b'2560' b'274' b'40104'
b'231508' b'49992' b'9572610' b'116376' b'322' b'2059202' b'42232'
b'5449896' b'2283888' b'851600' b'47008' b'341932' b'8460192' b'1762012'
b'8919972' b'308898' b'9253342' b'5058274' b'3069652' b'39396' b'41190'
b'1701892' b'151338' b'158824' b'12962' b'2775016' b'445134' b'2258354'
b'2508064' b'7029696' b'268680' b'9346750' b'4220844' b'8716' b'2144600'
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b'12914' b'180376' b'4215708' b'346562' b'4213864' b'150' b'611150'
b'341564' b'73336' b'636190' b'228892' b'4050686' b'418046' b'4217848'
b'9087796' b'124106' b'42326' b'43190' b'7602868' b'3222166' b'4559600'
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b'8972988' b'13044' b'2602716' b'339454' b'1431556' b'10930' b'7938660'
b'9645742' b'8317256' b'5325926' b'132' b'1008924' b'12906' b'37576'
b'7804492' b'43856' b'5733092' b'5437546' b'42452' b'8527452' b'4372262'
b'16086' b'54' b'42784' b'2906766' b'39954' b'12830' b'12910' b'1332120'
b'5663142' b'1454538' b'8786792' b'1148858' b'874058' b'40430' b'42844'
b'2803540' b'7274928' b'5006352' b'172330' b'964808' b'313734' b'53790'
b'8295482' b'1350484' b'9120462' b'308' b'8532322' b'896422' b'10210472'
b'8069214' b'40696' b'130144' b'105228' b'8423364' b'2727130' b'613082'
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b'299666' b'2711250' b'8716280' b'4095390' b'3690' b'266' b'52164'
b'3404398' b'40326' b'2950662' b'576150' b'9173188' b'8180554' b'12952'
b'580012' b'508024' b'43666' b'7966810' b'948804' b'663212' b'8499334'
b'8061706' b'49286' b'3808' b'7881562' b'57268' b'46400' b'332402'
b'8058340' b'1322894' b'7763546' b'6402514' b'722168' b'332334'
b'6214036' b'24340' b'35094' b'5416586' b'2565688' b'166608' b'48176'
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b'4134756' b'7949542' b'797244' b'3400600' b'243662' b'195944' b'207132'
b'1261946' b'61910' b'12880' b'1693480' b'232' b'52132' b'548628'
b'45480' b'4200516' b'1919968' b'265176' b'850780' b'44042' b'234316'
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b'3106488' b'10379954' b'7334412' b'680442' b'5754078' b'6551786'
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b'3223828' b'2348204' b'1834646' b'9404992' b'95252' b'40180' b'2978858'
b'1639972' b'143928' b'1200390' b'376562' b'41092' b'86834' b'7659560'
b'1866' b'1546508' b'9176102' b'3721646' b'264586' b'48064' b'706158'
b'44154' b'1245808' b'151508' b'43456' b'43310' b'5382034' b'5689306'
b'762102' b'3956196' b'42466' b'195266' b'590686' b'530258' b'43496'
b'393802' b'45916' b'45384' b'2376' b'6566018' b'657838' b'147078'
b'185670' b'1993624' b'44434' b'40478' b'28340' b'9088890' b'310838'
b'9480124' b'53834' b'477574' b'45612' b'994596' b'431244' b'7086294'
b'269346' b'543774' b'1686384' b'5566196' b'3605846' b'43264' b'276'
b'8777612' b'359912' b'9485732' b'6769736' b'44744' b'115024' b'40082'
b'5721248' b'755690' b'624326' b'233502' b'44288' b'180' b'164570'
b'130534' b'1168952' b'294' b'870212' b'1214010' b'40714' b'119648'
b'467442' b'42650' b'46158' b'44840' b'39944' b'265744' b'3684446'
b'8645336' b'48252' b'272' b'9949618' b'45432' b'46476' b'37580' b'162'
b'7715552' b'44130' b'2416768' b'325264' b'188720' b'46988' b'131396'
b'231106' b'2326048' b'152022' b'45736' b'9901020' b'400144' b'732662'
b'751438' b'332102' b'366048' b'844102' b'121590' b'1165844' b'42344'
b'40840' b'9635830' b'22' b'7930874' b'125426' b'254' b'42922' b'462954'
b'1091510' b'74930' b'29434' b'40830' b'5063234' b'53192' b'12970'
b'851232' b'640460' b'196' b'265720' b'39190' b'1148866' b'9224874'
b'1225456' b'146580' b'9326534' b'999490' b'115960' b'823450' b'5091926'
b'46914' b'9319188' b'142368' b'7006352' b'7434996' b'1149362' b'1882452'
b'460948' b'6542998' b'6194036' b'596054' b'1422648' b'387560' b'304862'
b'28346' b'33670' b'374700' b'3570442' b'1745166' b'5033204' b'42330'
b'2166702' b'167458' b'13400' b'731450' b'2913876' b'202' b'13070'
b'44274' b'9489092' b'6283362' b'109570' b'42172' b'9493760' b'1667226'
b'53402' b'43482' b'744608' b'5862740' b'156770' b'3864516' b'159106'
b'192' b'38314' b'34786' b'74894' b'3603276' b'4886474' b'588436'
b'57838' b'773094' b'967110' b'312356' b'9676594' b'2849856' b'9172576'
b'6896848' b'41276' b'353386' b'7141192' b'495436' b'9391646' b'753796'
b'9986782' b'126006' b'4331106' b'6069720' b'3583172' b'46776' b'42736'
b'224640' b'922256' b'42722' b'8538678' b'12966' b'453186' b'2790012'
b'40938' b'3044676' b'2008128' b'5634786' b'236918' b'3626084' b'8359702'
b'2809316' b'3989940' b'1079580' b'10233910' b'39470' b'43792' b'7808958'
b'2964666' b'245982' b'4296148' b'1395516' b'523170' b'90' b'40968']
Feature: top_1_7_day_fresh_product
Tensor: [b'3860' b'1264' b'44' b'1260' b'NA' b'156' b'1448' b'176' b'13416'
b'8764' b'811012' b'100' b'51224' b'438616' b'1164' b'48' b'3500'
b'41608' b'1532' b'2124' b'57776' b'7732' b'1192' b'6716' b'811020'
b'79080' b'74448' b'4224' b'1180' b'555620' b'41428' b'148984' b'6732'
b'5996' b'4452' b'2764' b'6936' b'66816' b'188' b'72512' b'67576'
b'110564' b'3160' b'5752' b'52' b'420336' b'11792' b'2116' b'3048'
b'1376' b'8328' b'2312' b'949864' b'2500' b'2564' b'3176' b'10884'
b'87484' b'732924' b'49792' b'2120' b'4744' b'1340' b'2320' b'49776'
b'90068' b'49384' b'64836' b'66104' b'2692' b'1504' b'3080' b'555576'
b'264088' b'9272' b'41432' b'1372' b'811076' b'2720' b'77312' b'3188'
b'556188' b'41768' b'64128' b'245408' b'4416' b'7324' b'6000' b'6724'
b'121676' b'3376' b'1444' b'61244' b'68100' b'71800' b'9264' b'3356'
b'2316' b'76108' b'49780' b'88576' b'1172' b'123724' b'93652' b'71364'
b'32824' b'65464' b'3184' b'5552' b'67956' b'7128' b'81188' b'52112'
b'52140' b'2160' b'3504' b'57700' b'8248' b'458128' b'67940' b'151976'
b'49600' b'188700' b'81764' b'88740' b'429736' b'48012' b'4348' b'9852'
b'5712' b'5092' b'52084' b'83576' b'65568' b'41452' b'8072' b'73000'
b'914204' b'6124' b'2596']
Feature: top_2_7_day_fresh_product
Tensor: [b'44' b'8520' b'176' b'2120' b'NA' b'188' b'3356' b'1164' b'1260'
b'811428' b'2764' b'13416' b'1444' b'2692' b'1264' b'1376' b'41452'
b'133116' b'96956' b'2116' b'156' b'327444' b'4744' b'48012' b'14468'
b'52' b'1192' b'2564' b'66308' b'1372' b'57704' b'811772' b'3500'
b'462508' b'2124' b'8764' b'41432' b'52108' b'555748' b'148984' b'1176'
b'2312' b'9856' b'7324' b'49808' b'4224' b'4416' b'9264' b'100' b'3160'
b'110564' b'88740' b'1172' b'452212' b'4912' b'6640' b'3080' b'59128'
b'78348' b'9852' b'259324' b'6128' b'5692' b'93652' b'49784' b'70176'
b'48' b'5752' b'5092' b'5452' b'732952' b'811076' b'3316' b'41120'
b'49792' b'64128' b'3184' b'3732' b'76108' b'49616' b'10920' b'1504'
b'811012' b'1532' b'49788' b'6724' b'1180' b'3504' b'41428' b'2316'
b'74796' b'49384' b'41756' b'71800' b'452304' b'811120' b'215600' b'9568'
b'3176' b'235472' b'144528' b'67052' b'65964' b'5744' b'458128' b'5996'
b'811192' b'16296' b'121676' b'10000' b'4348' b'66924' b'65564' b'62604'
b'90024' b'2720' b'64132' b'4828' b'12872' b'77312' b'6732' b'3020'
b'1448' b'61244' b'3048' b'6936' b'64836' b'811024' b'3376' b'7732'
b'7872' b'2320' b'188620' b'74448' b'67116' b'41380' b'66816' b'65144'
b'67956' b'90068' b'54124' b'78388' b'4840' b'54148' b'7128' b'446792'
b'717000' b'944936' b'49780' b'16336' b'49800' b'37132' b'452220' b'2560'
b'60580' b'452240' b'839892' b'452244' b'41540' b'151976' b'41352'
b'41324' b'112436' b'67576' b'70448' b'214460' b'11792' b'41216' b'2204'
b'54160']
Feature: top_3_7_day_fresh_product
Tensor: [b'156' b'176' b'1260' b'78400' b'188' b'NA' b'1164' b'4452' b'74448'
b'1264' b'2116' b'811020' b'327444' b'41452' b'2764' b'33652' b'4744'
b'6936' b'1372' b'106660' b'62604' b'2316' b'3356' b'3160' b'148984'
b'66572' b'93652' b'3048' b'2500' b'67116' b'100' b'77312' b'2312'
b'2120' b'70412' b'1444' b'44' b'1532' b'49808' b'1176' b'1172' b'52'
b'48' b'555784' b'1192' b'11152' b'49816' b'7324' b'77808' b'11596'
b'1448' b'5048' b'1376' b'2692' b'2124' b'41204' b'74796' b'14468'
b'67956' b'10652' b'59128' b'613360' b'41768' b'3860' b'41464' b'4348'
b'75904' b'41776' b'3188' b'8248' b'49464' b'9856' b'49800' b'5752'
b'66308' b'70940' b'54124' b'811964' b'86792' b'4828' b'71800' b'1504'
b'555576' b'7872' b'4416' b'3080' b'90068' b'76108' b'49456' b'64132'
b'10920' b'8764' b'37120' b'65568' b'452324' b'5744' b'3176' b'86852'
b'48012' b'6640' b'41380' b'64128' b'110564' b'54160' b'2720' b'64572'
b'9568' b'2320' b'3184' b'41604' b'96956' b'3364' b'41428' b'452220'
b'67268' b'3500' b'452352' b'9272' b'2564' b'67576' b'120284' b'70448'
b'2160' b'6124' b'8168' b'77516' b'6724' b'41312' b'605148' b'188700'
b'3504' b'99268' b'452244' b'6128' b'1180' b'5692' b'52188' b'959780'
b'90024' b'185620' b'71284' b'555588' b'103652' b'2596' b'438616'
b'242736' b'458128' b'336724' b'13416' b'37136' b'77544' b'4836' b'85268'
b'65564' b'4224' b'51224' b'10000' b'564916' b'41120' b'65464']
Feature: top_4_7_day_fresh_product
Tensor: [b'1260' b'2160' b'1264' b'79028' b'NA' b'4912' b'2124' b'1164' b'4828'
b'2316' b'156' b'32824' b'9232' b'2692' b'3188' b'93652' b'100' b'2204'
b'64836' b'4224' b'77684' b'176' b'9264' b'3176' b'14652' b'1376' b'2120'
b'3160' b'8248' b'1532' b'1448' b'3500' b'90068' b'6640' b'9856' b'2564'
b'1176' b'188' b'8520' b'2312' b'54124' b'1504' b'74448' b'2764' b'44'
b'52' b'3356' b'4744' b'1172' b'52096' b'110564' b'5048' b'438616'
b'1180' b'185620' b'77312' b'62604' b'361736' b'4452' b'1444' b'2116'
b'1192' b'5744' b'3504' b'11260' b'10920' b'65884' b'48' b'1340' b'6732'
b'94608' b'41428' b'52112' b'41352' b'13416' b'452352' b'6724' b'7920'
b'3184' b'41432' b'41324' b'2500' b'70372' b'4416' b'14468' b'11152'
b'3732' b'1372' b'65144' b'8328' b'49612' b'235772' b'7324' b'61244'
b'77516' b'66924' b'67940' b'33736' b'2720' b'78400' b'7732' b'98700'
b'41388' b'1156' b'452304' b'54148' b'259324' b'11792' b'49640' b'7480'
b'67956' b'787368' b'99560' b'65568' b'52140' b'108772' b'780612'
b'264088' b'6936' b'103652' b'49800' b'3080' b'452244' b'142956' b'76108'
b'5452' b'3168' b'988024' b'10000' b'71800' b'41312' b'88740' b'811008'
b'235472' b'65132' b'613360' b'34476' b'41756' b'67052' b'5568' b'74796'
b'188700' b'148984' b'214460' b'5724' b'78288' b'816568' b'144528'
b'59128' b'112436']
Feature: top_5_7_day_fresh_product
Tensor: [b'176' b'2320' b'2316' b'NA' b'9856' b'1372' b'66924' b'3080' b'44'
b'5552' b'2312' b'1260' b'452300' b'2564' b'3732' b'4744' b'110564'
b'1448' b'2764' b'41428' b'52' b'4452' b'148984' b'3356' b'8764' b'6640'
b'1264' b'156' b'4416' b'48' b'8520' b'2116' b'2692' b'3860' b'5996'
b'7128' b'11152' b'74448' b'10432' b'3160' b'61244' b'3188' b'3376'
b'188620' b'9264' b'77312' b'2124' b'1164' b'1192' b'555576' b'1532'
b'5568' b'839892' b'5692' b'2120' b'4912' b'4828' b'11792' b'1176'
b'90024' b'151976' b'77000' b'188' b'1444' b'14468' b'74780' b'41204'
b'7324' b'75564' b'59128' b'49808' b'188700' b'52192' b'3504' b'811008'
b'77684' b'173104' b'5092' b'5744' b'90660' b'67956' b'8248' b'65564'
b'140080' b'81896' b'52140' b'822276' b'777184' b'4224' b'62604' b'7732'
b'76108' b'93652' b'64836' b'92396' b'103652' b'2324' b'52112' b'66520'
b'79028' b'74796' b'41120' b'70376' b'66820' b'259324' b'3364' b'1504'
b'2720' b'10304' b'41452' b'41756' b'41432' b'41604' b'64132' b'3500'
b'787456' b'64572' b'811108' b'65144' b'12884' b'9844' b'71800' b'787256'
b'57704' b'7872' b'78400' b'1172' b'811024' b'49780' b'214460' b'6716'
b'2596' b'1376' b'3048' b'3184' b'41464' b'57692' b'90068' b'85792'
b'361736' b'811428' b'100' b'41716' b'66104' b'37120' b'71260' b'33652'
b'6936' b'51224' b'215984' b'102644' b'235472']
Feature: top_1_15_day_fresh_product
Tensor: [b'1260' b'1264' b'44' b'176' b'156' b'NA' b'66924' b'13416' b'8764'
b'811020' b'100' b'51224' b'438616' b'6936' b'1164' b'2312' b'458128'
b'52' b'1340' b'2116' b'2124' b'77312' b'7128' b'3500' b'2120' b'2316'
b'4744' b'4224' b'3376' b'1532' b'1180' b'555620' b'235772' b'4416'
b'5048' b'2764' b'188' b'67576' b'110564' b'1444' b'11792' b'3356'
b'59128' b'1376' b'11260' b'10920' b'949864' b'1192' b'90068' b'5092'
b'10884' b'87484' b'732924' b'49792' b'3316' b'74448' b'93652' b'41452'
b'103652' b'3160' b'48' b'67116' b'76108' b'49384' b'1172' b'66104'
b'811012' b'3080' b'3504' b'41428' b'3732' b'49480' b'452244' b'555576'
b'264088' b'41756' b'1504' b'2376' b'811076' b'2720' b'1448' b'37120'
b'3188' b'556188' b'64656' b'41768' b'2692' b'452304' b'5744' b'7324'
b'16296' b'62604' b'2564' b'4348' b'148984' b'8328' b'9264' b'141148'
b'49780' b'88576' b'4912' b'7732' b'1372' b'6716' b'123724' b'9856'
b'41120' b'3860' b'54124' b'5552' b'67956' b'52112' b'2160' b'10648'
b'57700' b'2500' b'2320' b'6640' b'49800' b'1176' b'57708' b'67940'
b'151976' b'8168' b'49600' b'188700' b'81764' b'119944' b'922492'
b'48012' b'78400' b'5712' b'52084' b'83576' b'361736' b'4452' b'41324'
b'65568' b'10656' b'6732' b'914204' b'6124' b'2596']
Feature: top_2_15_day_fresh_product
Tensor: [b'44' b'8520' b'176' b'1164' b'NA' b'188' b'1264' b'41428' b'100' b'1260'
b'811428' b'2764' b'2316' b'1444' b'2692' b'2116' b'41452' b'133116'
b'3080' b'9264' b'2312' b'156' b'4744' b'14468' b'110564' b'2564'
b'57776' b'10884' b'7732' b'1372' b'1448' b'3500' b'88576' b'8764'
b'79080' b'1532' b'1376' b'11152' b'452352' b'555748' b'10816' b'41608'
b'1176' b'1192' b'7324' b'12064' b'4224' b'77312' b'2124' b'4452'
b'41120' b'41772' b'3160' b'88740' b'2596' b'48' b'2320' b'1172' b'74448'
b'420336' b'6640' b'9856' b'52' b'67956' b'6724' b'77000' b'246112'
b'2120' b'93652' b'6936' b'3184' b'732952' b'811076' b'188620' b'945464'
b'49776' b'64128' b'41768' b'3732' b'49616' b'54124' b'1504' b'65564'
b'452252' b'48012' b'70372' b'1180' b'66924' b'41540' b'9272' b'8328'
b'49384' b'71800' b'76108' b'71260' b'3356' b'5712' b'67052' b'65964'
b'452244' b'811192' b'438616' b'6000' b'85104' b'41380' b'787368' b'3188'
b'235472' b'10000' b'121676' b'87344' b'66820' b'10920' b'62604' b'61244'
b'452304' b'12872' b'2720' b'64836' b'7480' b'49800' b'3376' b'4416'
b'2160' b'6124' b'71364' b'4912' b'65568' b'787256' b'235772' b'11596'
b'11752' b'3176' b'581832' b'90068' b'81188' b'3504' b'52192' b'2500'
b'1340' b'2376' b'59128' b'7128' b'57704' b'446792' b'717000' b'944936'
b'8248' b'41716' b'458128' b'60580' b'839892' b'79028' b'4348' b'64132'
b'5744' b'151976' b'41352' b'2324' b'67576' b'16296' b'214460' b'70316'
b'87976' b'41216' b'2204' b'34484' b'54160']
Feature: top_3_15_day_fresh_product
Tensor: [b'156' b'176' b'1260' b'NA' b'1164' b'1448' b'44' b'7732' b'2116'
b'811012' b'327444' b'41452' b'2764' b'33652' b'452300' b'1264' b'1376'
b'48' b'2312' b'2692' b'3188' b'8764' b'41120' b'90068' b'452352' b'2120'
b'3160' b'148984' b'1532' b'41428' b'93652' b'188' b'1192' b'52' b'100'
b'67116' b'3364' b'6716' b'3048' b'945464' b'1372' b'2124' b'49808'
b'74448' b'613360' b'76108' b'1172' b'556188' b'555784' b'88576' b'66308'
b'9856' b'11152' b'62604' b'6936' b'41716' b'41324' b'65132' b'1444'
b'4828' b'77312' b'3860' b'452212' b'4912' b'4416' b'5752' b'86520'
b'2316' b'59128' b'78348' b'8328' b'1180' b'3356' b'556808' b'3020'
b'37128' b'6724' b'2564' b'5712' b'4348' b'67956' b'922528' b'8520'
b'49792' b'173104' b'9272' b'64836' b'2500' b'9852' b'1504' b'555576'
b'71356' b'74796' b'3080' b'65144' b'48012' b'49456' b'3504' b'64132'
b'57700' b'78400' b'1176' b'9568' b'3176' b'5744' b'67576' b'245408'
b'458128' b'14468' b'57708' b'86852' b'110564' b'73008' b'2720' b'41604'
b'96956' b'54148' b'48780' b'810996' b'120284' b'61244' b'52188' b'41768'
b'37168' b'41380' b'32824' b'3184' b'99268' b'49800' b'452244' b'179940'
b'54124' b'5692' b'4452' b'4840' b'52140' b'2596' b'41156' b'73264'
b'90024' b'41204' b'87344' b'71284' b'555588' b'7324' b'37132' b'48772'
b'88740' b'3732' b'336724' b'3500' b'37136' b'77544' b'188700' b'121676'
b'4836' b'85268' b'66816' b'5552' b'2376' b'90852' b'8072' b'51224'
b'11352' b'5724' b'7128' b'57704' b'564916' b'37124']
Feature: top_4_15_day_fresh_product
Tensor: [b'3860' b'2160' b'1264' b'1448' b'1260' b'NA' b'4452' b'74448' b'1164'
b'811944' b'13416' b'452324' b'1444' b'3500' b'41756' b'41608' b'452352'
b'93652' b'176' b'2124' b'11152' b'8764' b'44' b'4416' b'90024' b'3048'
b'2500' b'1372' b'2764' b'3176' b'2120' b'2116' b'3160' b'90068'
b'811772' b'1192' b'156' b'462508' b'1532' b'3184' b'110564' b'188' b'52'
b'14468' b'112436' b'2312' b'52096' b'3504' b'76108' b'66816' b'65964'
b'78348' b'438616' b'5048' b'2564' b'52188' b'3188' b'1376' b'2692'
b'2316' b'4224' b'41120' b'79028' b'1176' b'6936' b'1172' b'5744'
b'16344' b'64132' b'3080' b'9272' b'41768' b'6128' b'49784' b'100' b'48'
b'5752' b'62604' b'41428' b'1340' b'246112' b'49800' b'839892' b'66308'
b'2204' b'264088' b'10920' b'41324' b'37132' b'811964' b'49788' b'41604'
b'71800' b'1504' b'7872' b'3732' b'7732' b'78400' b'49612' b'4744'
b'7324' b'54124' b'6640' b'452304' b'811120' b'2320' b'41216' b'77516'
b'3356' b'4912' b'54148' b'555576' b'48012' b'7128' b'67116' b'74796'
b'4828' b'79064' b'64572' b'452244' b'9568' b'8328' b'452220' b'61244'
b'67268' b'49596' b'811024' b'787368' b'64764' b'57692' b'65564' b'8168'
b'605148' b'65144' b'103652' b'121676' b'11260' b'54160' b'76348' b'1180'
b'10000' b'64128' b'66924' b'811952' b'959780' b'2720' b'70136' b'16336'
b'811008' b'16280' b'235772' b'241064' b'49492' b'2560' b'452240'
b'337124' b'67956' b'429736' b'5568' b'9264' b'214460' b'922528'
b'151976' b'816568' b'144528' b'4200']
Feature: top_5_15_day_fresh_product
Tensor: [b'3500' b'2320' b'2316' b'2120' b'1264' b'NA' b'1372' b'4912' b'64132'
b'2312' b'14468' b'176' b'839892' b'2124' b'2116' b'3732' b'41452'
b'54148' b'62604' b'188' b'3356' b'327444' b'48012' b'9264' b'3176'
b'10816' b'1192' b'3160' b'4828' b'1164' b'156' b'66308' b'52' b'3860'
b'452244' b'100' b'1260' b'2564' b'1176' b'41432' b'1180' b'1532' b'2376'
b'54124' b'48' b'2692' b'3184' b'44' b'7324' b'77808' b'10920' b'85268'
b'361736' b'1172' b'41120' b'1444' b'1448' b'4744' b'3188' b'3080'
b'6936' b'88740' b'151976' b'2764' b'3504' b'3048' b'65884' b'75756'
b'76108' b'74448' b'103652' b'2500' b'1340' b'59128' b'49808' b'41464'
b'71052' b'52084' b'1376' b'8248' b'452352' b'7920' b'16312' b'67116'
b'70940' b'86792' b'188700' b'52140' b'188620' b'556808' b'235772'
b'7732' b'5996' b'61244' b'47984' b'92396' b'2560' b'3364' b'120284'
b'5552' b'2324' b'79028' b'52112' b'33736' b'2720' b'71260' b'74796'
b'13416' b'57776' b'41604' b'66820' b'93652' b'11152' b'10304' b'4416'
b'452304' b'259324' b'11792' b'787456' b'70448' b'64128' b'77516' b'8764'
b'780612' b'52192' b'110564' b'57704' b'811192' b'41428' b'78400'
b'49796' b'67044' b'4224' b'142956' b'3168' b'41716' b'64536' b'5692'
b'185620' b'90068' b'235472' b'78132' b'1504' b'438616' b'5048' b'77312'
b'64656' b'67956' b'840108' b'7128' b'41776' b'6724' b'65464' b'68224']
Feature: top_1_60_day_fresh_product
Tensor: [b'1260' b'1264' b'44' b'176' b'8536' b'66924' b'184' b'8764' b'811020'
b'100' b'1164' b'438616' b'2116' b'2692' b'41608' b'49600' b'2312' b'156'
b'NA' b'1340' b'52108' b'5716' b'6640' b'4416' b'77312' b'9264' b'7128'
b'3500' b'1376' b'76108' b'4224' b'1172' b'1180' b'73260' b'98784'
b'1192' b'9856' b'7324' b'3160' b'12064' b'5048' b'452244' b'613076'
b'70376' b'48' b'188' b'452212' b'110564' b'3356' b'2124' b'2120' b'52'
b'1444' b'41464' b'59128' b'77000' b'5552' b'246112' b'556808' b'65884'
b'74448' b'2500' b'1532' b'57700' b'2564' b'41432' b'732952' b'49792'
b'3316' b'41452' b'67116' b'49788' b'3080' b'48012' b'6724' b'2764'
b'3504' b'41428' b'49480' b'2560' b'74796' b'2316' b'41756' b'1504'
b'6832' b'811076' b'2720' b'1176' b'556188' b'14672' b'41768' b'65964'
b'452304' b'16296' b'62604' b'4348' b'41324' b'41120' b'34484' b'71260'
b'5744' b'96956' b'61244' b'3048' b'49780' b'461668' b'103652' b'52188'
b'1448' b'65568' b'37128' b'49596' b'64132' b'67956' b'811024' b'6716'
b'65144' b'1372' b'3184' b'71800' b'9272' b'529128' b'54124' b'452368'
b'811008' b'49808' b'8168' b'51224' b'151976' b'60580' b'2596' b'88740'
b'78400' b'2160' b'839892' b'83576' b'5712' b'52084' b'361736' b'66104'
b'10920' b'90068' b'5724' b'914204' b'6936' b'144528' b'4200']
Feature: top_2_60_day_fresh_product
Tensor: [b'44' b'176' b'1264' b'1260' b'NA' b'156' b'2560' b'11596' b'100' b'1164'
b'811428' b'2316' b'2596' b'41452' b'133116' b'65884' b'65464' b'2116'
b'2124' b'2764' b'4744' b'64572' b'93652' b'8244' b'88740' b'3500'
b'2564' b'179940' b'7732' b'74448' b'6716' b'3048' b'3176' b'2120'
b'8764' b'9856' b'4416' b'1532' b'3376' b'2312' b'1448' b'14672' b'65568'
b'5048' b'54148' b'70940' b'49808' b'4224' b'77312' b'7324' b'5752'
b'41120' b'564896' b'1172' b'67576' b'142956' b'3160' b'6936' b'811944'
b'3504' b'57692' b'188' b'65128' b'8328' b'2320' b'1176' b'52' b'85104'
b'4828' b'52140' b'5712' b'10884' b'87484' b'811076' b'67940' b'86520'
b'246112' b'452304' b'1192' b'839892' b'7128' b'48' b'148984' b'1372'
b'1504' b'41324' b'2720' b'811012' b'49788' b'110564' b'52188' b'66924'
b'3188' b'49388' b'452244' b'52156' b'9272' b'121676' b'41756' b'2500'
b'41428' b'52096' b'82408' b'3080' b'37120' b'66820' b'67052' b'438616'
b'245408' b'2692' b'86852' b'4348' b'4452' b'1444' b'67116' b'3184'
b'65564' b'64128' b'3364' b'9264' b'10920' b'26792' b'82868' b'452324'
b'90068' b'49596' b'66308' b'52108' b'7480' b'810996' b'88576' b'4912'
b'77652' b'65144' b'1180' b'3356' b'81676' b'581832' b'51224' b'2324'
b'81188' b'52192' b'5996' b'2376' b'184' b'6724' b'2204' b'446792'
b'76348' b'107676' b'49780' b'49800' b'103652' b'8168' b'64764' b'48012'
b'41432' b'79028' b'5552' b'49604' b'452300' b'5092' b'82940' b'811000'
b'62604' b'3732' b'76108' b'57776' b'66104' b'6124']
Feature: top_3_60_day_fresh_product
Tensor: [b'156' b'2312' b'1260' b'44' b'100' b'NA' b'176' b'5744' b'4348' b'1164'
b'811012' b'3160' b'41452' b'1376' b'33652' b'52140' b'2116' b'1264'
b'77312' b'7872' b'66308' b'458128' b'1172' b'2316' b'74448' b'3364'
b'14468' b'141704' b'188' b'1372' b'2764' b'4416' b'246112' b'12064'
b'1532' b'88576' b'4224' b'811020' b'48784' b'4828' b'49808' b'1180'
b'9264' b'52' b'452352' b'2564' b'73264' b'2500' b'148984' b'3504'
b'1192' b'6124' b'3188' b'6936' b'41716' b'2124' b'839892' b'564912'
b'41380' b'7128' b'949664' b'62604' b'90676' b'556808' b'11908' b'6724'
b'11260' b'77000' b'48' b'7324' b'8764' b'2320' b'77808' b'2120' b'67956'
b'1444' b'71052' b'6716' b'8240' b'5048' b'188620' b'71800' b'41428'
b'41540' b'3080' b'1448' b'847168' b'64128' b'41768' b'54124' b'1408'
b'2692' b'452252' b'70372' b'71356' b'49472' b'90068' b'70424' b'264088'
b'8520' b'65144' b'3184' b'2560' b'2376' b'11596' b'6640' b'4744' b'3168'
b'81188' b'73008' b'811192' b'67576' b'6000' b'74796' b'85104' b'57776'
b'787368' b'452304' b'121676' b'64132' b'87344' b'2720' b'88740' b'3356'
b'41604' b'9272' b'8328' b'67940' b'54148' b'3732' b'49596' b'811024'
b'555576' b'1504' b'452220' b'65568' b'787256' b'1176' b'52192' b'110564'
b'9856' b'57692' b'49800' b'65548' b'59128' b'41756' b'49488' b'5752'
b'3048' b'2596' b'76108' b'5552' b'41732' b'65464' b'93652' b'66924'
b'904620' b'57700' b'7732' b'811772' b'452240' b'65296' b'48772' b'81764'
b'151976' b'64764' b'3176' b'49792' b'33644' b'83576' b'2160' b'41464'
b'605148' b'6732' b'41216' b'34484' b'54160']
Feature: top_4_60_day_fresh_product
Tensor: [b'176' b'8520' b'1264' b'NA' b'1260' b'2692' b'1372' b'13416' b'188'
b'2116' b'811944' b'1164' b'41768' b'1444' b'3080' b'156' b'1448' b'48'
b'52' b'5048' b'77312' b'100' b'2120' b'44' b'74448' b'4452' b'2764'
b'2312' b'2500' b'3160' b'581832' b'10884' b'1532' b'3364' b'452240'
b'41428' b'462508' b'1176' b'438616' b'62604' b'14468' b'2316' b'3176'
b'246112' b'11152' b'2124' b'9264' b'1192' b'3504' b'110564' b'41324'
b'2564' b'5744' b'179940' b'41540' b'6936' b'1376' b'67956' b'65884'
b'1172' b'88740' b'41216' b'79064' b'76108' b'59128' b'37132' b'3184'
b'10432' b'9272' b'732924' b'4744' b'71264' b'452304' b'49776' b'7128'
b'839900' b'184' b'8764' b'78400' b'66104' b'41432' b'6320' b'48636'
b'4416' b'7872' b'8328' b'49600' b'49816' b'3732' b'79636' b'77544'
b'79028' b'41196' b'41120' b'66308' b'9844' b'4828' b'41604' b'452212'
b'7732' b'3860' b'185620' b'70376' b'74796' b'90068' b'839892' b'3500'
b'65548' b'93652' b'3356' b'3188' b'6732' b'86792' b'452220' b'1180'
b'11792' b'9568' b'3168' b'52140' b'845268' b'41716' b'61244' b'11752'
b'11908' b'6124' b'8168' b'79080' b'5552' b'41172' b'52156' b'613360'
b'108772' b'564916' b'64772' b'5692' b'86852' b'52112' b'2560' b'103652'
b'64128' b'121676' b'5996' b'2720' b'605420' b'2596' b'49784' b'3048'
b'555576' b'10648' b'87344' b'11596' b'33708' b'810996' b'5860' b'57708'
b'88576' b'49388' b'188700' b'119944' b'2320' b'67116' b'51224' b'6832'
b'811076' b'151976' b'214460' b'97240' b'84120' b'92396' b'816568'
b'78388' b'1504' b'37124' b'8244']
Feature: top_5_60_day_fresh_product
Tensor: [b'1264' b'156' b'2316' b'1164' b'44' b'NA' b'188' b'2120' b'64572'
b'1172' b'1260' b'78388' b'1448' b'65144' b'6936' b'2312' b'140080'
b'2764' b'176' b'64836' b'4416' b'13416' b'3048' b'3500' b'7324' b'2692'
b'5752' b'2124' b'3364' b'100' b'6640' b'2116' b'51224' b'90068' b'9856'
b'48' b'41812' b'1192' b'41432' b'74448' b'452244' b'52' b'1532'
b'452300' b'5552' b'1176' b'52096' b'62604' b'438616' b'4452' b'66816'
b'41732' b'3188' b'2596' b'41120' b'3160' b'64128' b'1376' b'1444'
b'811052' b'11792' b'3356' b'245720' b'71264' b'1180' b'70372' b'2564'
b'76108' b'70176' b'77544' b'811000' b'41756' b'41428' b'945464'
b'787256' b'1372' b'581832' b'5048' b'3080' b'3184' b'5996' b'3504'
b'3732' b'264088' b'452324' b'49384' b'9272' b'41708' b'9852' b'86792'
b'41604' b'4828' b'5716' b'14468' b'41540' b'11260' b'59128' b'67940'
b'88740' b'8764' b'3860' b'93652' b'41716' b'103652' b'2320' b'839900'
b'64132' b'4348' b'65084' b'5744' b'52140' b'6124' b'2720' b'78400'
b'41380' b'787456' b'67116' b'206252' b'3176' b'8328' b'452220' b'452304'
b'66308' b'2308' b'556188' b'65296' b'603460' b'811968' b'65384' b'57700'
b'603440' b'65564' b'8248' b'64968' b'41768' b'71800' b'65108' b'7732'
b'605148' b'71260' b'77312' b'4912' b'246112' b'16344' b'9264' b'811428'
b'79028' b'67956' b'76348' b'5692' b'7872' b'49804' b'845268' b'2500'
b'2160' b'839892' b'37172' b'4744' b'110564' b'41724' b'109768' b'185620'
b'12356' b'10920' b'1340' b'377356' b'86520' b'458128' b'54124' b'49600'
b'12872' b'33652' b'41216' b'4224' b'78432' b'86852' b'607836' b'65568'
b'70304' b'277204' b'5712' b'74796' b'6724' b'77516' b'2560']
Feature: top_1_90_day_fresh_product
Tensor: [b'1260' b'1264' b'44' b'66924' b'176' b'77000' b'11596' b'811020' b'100'
b'1164' b'438616' b'41452' b'65884' b'49600' b'2312' b'2116' b'1376'
b'NA' b'8764' b'1340' b'2500' b'5716' b'3500' b'6640' b'4416' b'77312'
b'12064' b'9264' b'74448' b'7128' b'156' b'52' b'76108' b'4224' b'1172'
b'1180' b'14672' b'98784' b'9856' b'7324' b'70940' b'452244' b'613076'
b'564896' b'2316' b'48788' b'2124' b'102640' b'110564' b'3356' b'2120'
b'2764' b'1444' b'59128' b'5552' b'246112' b'1448' b'3160' b'1532'
b'57704' b'2564' b'41432' b'732952' b'49792' b'1192' b'3316' b'839892'
b'67116' b'48012' b'6724' b'2204' b'3504' b'41428' b'49480' b'2560'
b'41756' b'1372' b'1504' b'6832' b'2720' b'1176' b'556188' b'48'
b'452304' b'16296' b'4452' b'62604' b'4348' b'41324' b'41120' b'188'
b'34484' b'88740' b'96956' b'61244' b'11792' b'49780' b'5744' b'461668'
b'3184' b'103652' b'52188' b'77652' b'49596' b'8328' b'67956' b'811428'
b'6716' b'3080' b'65144' b'2376' b'57700' b'71800' b'9272' b'529128'
b'54124' b'811008' b'86520' b'52140' b'8168' b'88576' b'2596' b'78400'
b'41768' b'2160' b'52084' b'83576' b'361736' b'66104' b'10920' b'90068'
b'914204' b'54160' b'6936' b'144528' b'4200']
Feature: top_2_90_day_fresh_product
Tensor: [b'44' b'176' b'1260' b'2204' b'156' b'1264' b'3364' b'8764' b'13416'
b'1164' b'811012' b'2316' b'2596' b'2692' b'140080' b'65464' b'NA'
b'2764' b'4744' b'64572' b'93652' b'3048' b'52108' b'8244' b'2564'
b'108772' b'2124' b'581832' b'7732' b'3500' b'1192' b'74448' b'4224'
b'4828' b'2120' b'9856' b'4416' b'1532' b'1448' b'2116' b'73264' b'3080'
b'49808' b'2312' b'839892' b'12872' b'48784' b'564912' b'100' b'41432'
b'188' b'67576' b'142956' b'3160' b'6936' b'4348' b'86520' b'57692'
b'59128' b'556808' b'78348' b'3504' b'11260' b'52' b'85104' b'52140'
b'6732' b'5712' b'10884' b'1172' b'71052' b'732924' b'811076' b'452352'
b'71800' b'780612' b'1376' b'77312' b'1372' b'148984' b'41324' b'37132'
b'246112' b'52188' b'3188' b'49388' b'74796' b'52156' b'9272' b'8328'
b'121676' b'1180' b'2500' b'41428' b'2320' b'82408' b'37120' b'66820'
b'110564' b'14672' b'185620' b'67052' b'5448' b'70412' b'86852' b'9264'
b'787368' b'452304' b'66924' b'65564' b'41120' b'64128' b'11792' b'1504'
b'88740' b'5744' b'452212' b'82868' b'3184' b'452324' b'90068' b'14468'
b'49596' b'48' b'48776' b'1444' b'98700' b'65144' b'6124' b'1176'
b'922492' b'65568' b'52192' b'51224' b'605420' b'79028' b'67956' b'5048'
b'81188' b'70740' b'2720' b'184' b'811008' b'6724' b'7128' b'41732'
b'446792' b'107676' b'438616' b'6640' b'810996' b'76108' b'71260'
b'151976' b'52096' b'64764' b'60580' b'7324' b'3176' b'49604' b'57700'
b'5092' b'52084' b'82940' b'62604' b'41352' b'41756' b'5552' b'41452'
b'57776' b'84120' b'41216' b'10920' b'5724']
Feature: top_3_90_day_fresh_product
Tensor: [b'156' b'2312' b'1260' b'1264' b'176' b'1164' b'5744' b'1372' b'85104'
b'77000' b'811428' b'3160' b'41452' b'33652' b'2116' b'77312' b'44'
b'1448' b'7872' b'458128' b'2564' b'2124' b'NA' b'1172' b'3364' b'67940'
b'14468' b'141704' b'188' b'88740' b'246112' b'100' b'2596' b'811020'
b'3504' b'49808' b'74448' b'1376' b'37132' b'452352' b'2764' b'1532'
b'73260' b'9264' b'54148' b'2500' b'5996' b'4416' b'1192' b'6936' b'2120'
b'564952' b'7128' b'949664' b'62604' b'90676' b'82408' b'6640' b'41464'
b'3048' b'1444' b'76108' b'8328' b'1180' b'2692' b'52' b'8764' b'48'
b'77808' b'67956' b'9272' b'2316' b'87484' b'6716' b'65548' b'452304'
b'93652' b'3080' b'67116' b'64128' b'148984' b'452324' b'1504' b'1408'
b'65084' b'86792' b'49788' b'70372' b'110564' b'59128' b'49472' b'7732'
b'65564' b'90068' b'264088' b'65144' b'41756' b'71800' b'52096' b'2376'
b'41120' b'839900' b'438616' b'81188' b'103652' b'73008' b'9568'
b'185620' b'74796' b'64132' b'87344' b'7324' b'235472' b'71260' b'3500'
b'10920' b'68100' b'3356' b'41604' b'2720' b'41428' b'52108' b'3188'
b'88576' b'4912' b'3376' b'41716' b'4828' b'452220' b'65568' b'9856'
b'10652' b'581832' b'4224' b'3176' b'11908' b'98784' b'117632' b'4744'
b'57692' b'68104' b'52156' b'2204' b'2320' b'3184' b'452368' b'49780'
b'57700' b'65296' b'81764' b'41432' b'4348' b'5552' b'66104' b'452300'
b'51224' b'121676' b'83576' b'2160' b'811024' b'555576' b'34484' b'6124']
Feature: top_4_90_day_fresh_product
Tensor: [b'176' b'1192' b'1264' b'100' b'49808' b'1164' b'74448' b'44' b'2120'
b'214460' b'156' b'85104' b'811944' b'41768' b'1376' b'2692' b'3080'
b'1260' b'52140' b'2116' b'48' b'188' b'52' b'2312' b'7732' b'NA' b'2764'
b'5048' b'235472' b'3364' b'90068' b'3500' b'7324' b'4416' b'1372'
b'14468' b'2316' b'10884' b'1532' b'6716' b'9856' b'3048' b'3160'
b'462508' b'438616' b'1180' b'5744' b'9264' b'1444' b'3376' b'2564'
b'14652' b'3176' b'1176' b'65568' b'41224' b'246112' b'11152' b'2124'
b'1448' b'5752' b'3188' b'70376' b'77312' b'41120' b'1172' b'78432'
b'4452' b'245720' b'65128' b'65884' b'787256' b'79064' b'8520' b'4912'
b'2204' b'70176' b'8240' b'4744' b'71264' b'49776' b'7128' b'452304'
b'839892' b'3504' b'5092' b'9272' b'54124' b'2500' b'66104' b'110564'
b'41432' b'2720' b'6320' b'452252' b'7872' b'92116' b'66924' b'121676'
b'49600' b'8244' b'452244' b'70424' b'65564' b'76108' b'3184' b'5568'
b'2560' b'41324' b'65964' b'3732' b'6000' b'4828' b'74796' b'57776'
b'41716' b'3168' b'8764' b'65548' b'811940' b'2596' b'6936' b'839900'
b'452220' b'9568' b'8328' b'555576' b'6732' b'67956' b'61244' b'65384'
b'93652' b'88740' b'41540' b'79080' b'41172' b'52156' b'251052' b'148984'
b'37128' b'811192' b'49800' b'64132' b'67044' b'2324' b'78400' b'14672'
b'49784' b'52108' b'5996' b'41428' b'555588' b'344784' b'5552' b'264088'
b'10656' b'64020' b'11596' b'33708' b'452352' b'62604' b'5860' b'57708'
b'811772' b'48028' b'564916' b'48772' b'49788' b'377356' b'556808'
b'581832' b'65144' b'64764' b'8248' b'151976' b'2320' b'97240' b'1504'
b'452300' b'107604' b'78388' b'37124']
Feature: top_5_90_day_fresh_product
Tensor: [b'1264' b'2316' b'176' b'44' b'1172' b'1260' b'7732' b'188' b'100'
b'64132' b'78388' b'1444' b'156' b'6936' b'2120' b'65084' b'2312' b'1164'
b'133116' b'41608' b'NA' b'93652' b'77312' b'74448' b'4452' b'2764' b'52'
b'67116' b'2124' b'3160' b'2692' b'3504' b'4416' b'179940' b'2116'
b'1372' b'1192' b'48' b'1176' b'1532' b'3080' b'452244' b'1376' b'452240'
b'5552' b'52096' b'5744' b'6124' b'41120' b'41324' b'564916' b'1448'
b'3364' b'64128' b'5752' b'811944' b'2564' b'3048' b'1180' b'3184'
b'3500' b'2560' b'76108' b'57700' b'4828' b'52108' b'64656' b'4348'
b'41716' b'452304' b'3860' b'41216' b'581832' b'811000' b'184' b'3188'
b'3732' b'11596' b'78400' b'557876' b'41708' b'438616' b'5692' b'49788'
b'2160' b'48636' b'86520' b'2320' b'8328' b'2500' b'71356' b'67940'
b'8764' b'79028' b'41196' b'5996' b'51224' b'811076' b'54124' b'66308'
b'9844' b'77244' b'41604' b'92396' b'7128' b'5860' b'90068' b'71792'
b'33724' b'3176' b'3356' b'1504' b'65564' b'41428' b'768752' b'54148'
b'839900' b'52140' b'810996' b'13416' b'65548' b'811772' b'11752'
b'78432' b'199836' b'452300' b'48648' b'64968' b'787256' b'67576'
b'41768' b'6640' b'246112' b'74796' b'59128' b'41756' b'108772' b'1156'
b'6716' b'839892' b'2596' b'49488' b'49804' b'845268' b'206252' b'26792'
b'110564' b'9568' b'41224' b'603460' b'65464' b'41724' b'185620' b'88576'
b'88740' b'10920' b'70176' b'377356' b'811428' b'8168' b'149008' b'49388'
b'12872' b'65144' b'33652' b'151976' b'49816' b'48012' b'14468' b'4744'
b'6832' b'33644' b'41432' b'71260' b'214460' b'605148' b'41204' b'816568'
b'77516' b'8244']
Feature: top_1_7_day_non_fresh_product
Tensor: [b'2192' b'124' b'1352' b'1356' b'1228' b'968504' b'8216' b'7424' b'1528'
b'736296' b'245488' b'67624' b'51504' b'5616' b'59608' b'88' b'9956'
b'40456' b'1332' b'2496' b'64616' b'3460' b'66460' b'11424' b'80460'
b'2556' b'8500' b'10104' b'455536' b'1380' b'399060' b'19740' b'1276'
b'4648' b'16696' b'37752' b'5344' b'28' b'1232' b'1560' b'2492' b'1184'
b'45244' b'2572' b'771148' b'2412' b'60928' b'24' b'2860' b'NA' b'1516'
b'4708' b'211568' b'2140' b'12672' b'1284' b'44796' b'2732' b'31068'
b'1240' b'1424' b'1324' b'1452' b'4408' b'116796' b'2676' b'7152' b'3060'
b'809012' b'14828' b'7108' b'64088' b'20' b'455204' b'48688' b'4748'
b'36584' b'53648' b'118652' b'313612' b'210984' b'8388' b'3056' b'2188'
b'4696' b'16600' b'1304' b'2776' b'25136' b'36328' b'75152' b'6348'
b'57152' b'6156' b'25128' b'300016' b'1344' b'92344' b'8104' b'70340'
b'6564' b'25108' b'70516' b'1328' b'7668' b'36568' b'1220' b'7224'
b'8592' b'4104' b'3124' b'2132' b'116452' b'4328' b'3808' b'24816'
b'915816' b'135372' b'51292' b'277808' b'49712' b'160' b'50552' b'2296'
b'326180' b'16680' b'3532' b'2704' b'4160' b'583488' b'6356' b'1512'
b'370204' b'4464' b'3976' b'3488' b'2488' b'6364' b'3596' b'1312'
b'36504' b'818192' b'50636' b'4948' b'2568' b'2584' b'108' b'4564'
b'84160' b'10756' b'65932' b'48664' b'2456' b'4924' b'12552' b'5388'
b'490528' b'5672' b'70292' b'65812' b'3092' b'84652' b'956856' b'32'
b'3200' b'2416' b'10808' b'51448' b'8556' b'41872' b'90236' b'557064'
b'1216' b'447028' b'75124' b'84372' b'3236' b'3744' b'65428' b'1248'
b'66564' b'9416' b'147824' b'32856' b'12136' b'2260' b'293556' b'11632'
b'33180' b'33412' b'49756' b'90296' b'4016' b'4780' b'2552' b'3668'
b'764628' b'26508' b'6944' b'1500' b'16668' b'3276' b'5596' b'45612'
b'7176' b'216276' b'63380' b'5080' b'6612' b'95460' b'44480' b'74684'
b'219788' b'7980' b'6052' b'11008' b'64228' b'25132' b'2112' b'2148'
b'12556' b'4816' b'36444' b'15604' b'2240' b'95320' b'51864' b'3920'
b'450356' b'989708' b'11836' b'77380' b'9544' b'957296' b'11732' b'10200'
b'557748' b'908208' b'77908' b'57148' b'28376' b'168' b'103520' b'36852'
b'14796' b'3784' b'1404' b'94716' b'103884' b'84' b'4196' b'251808' b'72'
b'51620' b'92284' b'24980' b'4812' b'60944' b'2508' b'2256' b'9884'
b'3484' b'83740' b'3720' b'7736' b'501784' b'83872' b'6512' b'776584'
b'76' b'14480' b'136' b'961820' b'4192' b'6212' b'2280' b'64824' b'12204'
b'16168' b'6748' b'3468' b'1360' b'113520' b'12532' b'10428' b'765808'
b'8704' b'12176' b'4704' b'89560' b'62252' b'11432' b'7960' b'6664'
b'5444' b'142452' b'5636' b'3144' b'5944' b'2688' b'1224' b'24836'
b'3564' b'6380' b'17784' b'15920' b'3344' b'5688' b'2348' b'2208'
b'51556' b'114072' b'68460' b'17528' b'3324' b'1552' b'17596' b'119164'
b'65424' b'48608' b'8644' b'597344' b'65968' b'903488' b'12436' b'1252'
b'3736' b'1368' b'52220' b'2848' b'95916' b'92164' b'65012' b'12544'
b'7228' b'73408' b'2332' b'214472' b'77936' b'286488' b'68' b'31060'
b'989268' b'1384' b'13636' b'373576' b'121212' b'1212' b'455356' b'44860'
b'5508' b'40396' b'7712' b'2268' b'379780' b'3028' b'5404' b'14524'
b'2304' b'24112' b'1556' b'65696' b'97332' b'6632' b'6816' b'10424'
b'4880' b'33276' b'87020' b'5704' b'200112' b'273364' b'54988' b'454264'
b'113336' b'6072' b'24932' b'96944' b'9132' b'2264' b'83596' b'64112'
b'4520' b'1256' b'2248' b'6524' b'38512' b'22320' b'2520' b'25384'
b'55156' b'2180' b'11052' b'8808' b'111148' b'809428' b'4560' b'5236'
b'91612' b'306644' b'107352' b'2532']
Feature: top_2_7_day_non_fresh_product
Tensor: [b'51504' b'51716' b'65436' b'1352' b'2456' b'2492' b'1356' b'23944'
b'2188' b'2192' b'5316' b'1764' b'4240' b'10104' b'1312' b'609960' b'124'
b'22260' b'65932' b'2132' b'14804' b'557064' b'7668' b'57152' b'1424'
b'8500' b'82400' b'1620' b'3600' b'28' b'1500' b'12556' b'2688' b'4816'
b'2572' b'12176' b'2180' b'25108' b'68484' b'3668' b'2140' b'14756'
b'17756' b'1560' b'4148' b'14764' b'2848' b'16800' b'173436' b'6668'
b'114296' b'9344' b'2584' b'40456' b'1276' b'1512' b'6844' b'1184'
b'3200' b'88' b'65828' b'1452' b'NA' b'149492' b'5080' b'48736' b'1360'
b'8592' b'16132' b'44872' b'36764' b'19740' b'495964' b'31116' b'1284'
b'4908' b'68' b'1588' b'4648' b'8216' b'1228' b'64136' b'65052' b'3092'
b'1220' b'1612' b'7736' b'122012' b'66564' b'2520' b'6612' b'3276'
b'3144' b'9308' b'42444' b'6348' b'2352' b'24' b'2296' b'4748' b'60856'
b'3824' b'66088' b'44480' b'917780' b'11928' b'83900' b'10808' b'2516'
b'968008' b'2416' b'2440' b'749928' b'11468' b'2412' b'951152' b'7108'
b'1188' b'293556' b'68496' b'51444' b'764100' b'2148' b'4220' b'5084'
b'399060' b'4520' b'3744' b'1272' b'596104' b'5136' b'8180' b'89028'
b'20' b'300016' b'4524' b'10036' b'12136' b'45572' b'78504' b'2704'
b'455536' b'98044' b'25132' b'49712' b'49732' b'3124' b'49760' b'2208'
b'4408' b'1556' b'2176' b'28840' b'71452' b'3056' b'4276' b'5616'
b'64680' b'61112' b'10756' b'31068' b'908208' b'4192' b'1344' b'19172'
b'4572' b'64088' b'1368' b'6848' b'1232' b'4812' b'105632' b'56' b'2260'
b'454264' b'36444' b'73076' b'54988' b'4000' b'4172' b'2676' b'67028'
b'82364' b'76200' b'3084' b'1212' b'5056' b'1464' b'5348' b'65428'
b'3516' b'2144' b'6352' b'1468' b'89688' b'36568' b'82244' b'11336'
b'70516' b'83740' b'2860' b'3196' b'4564' b'2672' b'227156' b'8868'
b'2508' b'12656' b'1536' b'66796' b'488608' b'25772' b'51620' b'173052'
b'37928' b'4104' b'1404' b'2556' b'4196' b'597420' b'36852' b'12272'
b'502228' b'65708' b'10896' b'8556' b'27232' b'11024' b'6356' b'2332'
b'10992' b'94712' b'10064' b'11924' b'76' b'565772' b'7912' b'66460'
b'785820' b'22160' b'184120' b'108936' b'2248' b'43932' b'8388' b'66448'
b'14628' b'11512' b'3660' b'5960' b'6568' b'100696' b'7880' b'32'
b'475956' b'2304' b'65816' b'66568' b'1332' b'1248' b'5704' b'80116'
b'61064' b'823960' b'28368' b'908244' b'51292' b'65836' b'71428' b'2388'
b'172' b'41872' b'76312' b'16600' b'2540' b'105900' b'816152' b'11236'
b'20180' b'7224' b'12848' b'1244' b'3028' b'36824' b'21824' b'777220'
b'12360' b'3344' b'14532' b'242756' b'51424' b'82604' b'64852' b'9956'
b'3596' b'444016' b'5688' b'4040' b'784484' b'1436' b'9872' b'2624'
b'12580' b'300012' b'955756' b'5116' b'3460' b'22916' b'176580' b'1324'
b'1488' b'64056' b'4292' b'58312' b'36848' b'24984' b'42608' b'44884'
b'988636' b'35988' b'108' b'11376' b'66956' b'5236' b'2512' b'66952'
b'145892' b'63996' b'71440' b'761392' b'489120' b'51756' b'11200'
b'530892' b'2484' b'43976' b'65664' b'71004' b'24816' b'40432' b'76144'
b'1396' b'50876' b'48600' b'25128' b'5508' b'8960' b'1584' b'54976'
b'70220' b'3236' b'10224' b'21512' b'12544' b'5636' b'10184' b'1328'
b'57148' b'65916' b'49180' b'55380' b'7984' b'83132' b'3868' b'16536'
b'19748' b'35452' b'964104' b'6132' b'65424' b'64824' b'916296' b'1240'
b'108712' b'16748' b'66348' b'5444' b'12184' b'3524' b'43108' b'214408'
b'91696' b'5232' b'11688' b'45204' b'48624' b'78848' b'43384' b'7492'
b'7744' b'24784' b'1320' b'5592' b'51448' b'601476' b'3284' b'65684'
b'70292' b'83460' b'9276' b'746308' b'70340' b'4936' b'4380' b'2348'
b'375392' b'36912' b'42912' b'12380' b'93044' b'17596' b'67244' b'9544'
b'551076' b'15920' b'2212' b'2532' b'54436' b'2732' b'4948' b'3108'
b'750428' b'753992' b'6364' b'3476' b'144788' b'5984' b'36328' b'122672'
b'1528' b'64664' b'1420' b'9408' b'84928' b'443368' b'24788' b'24116'
b'5736' b'3920' b'1572' b'5676' b'203036' b'2292' b'79408' b'16' b'5332'
b'34880' b'6156' b'8124' b'6452' b'3956' b'65812' b'279408' b'273356'
b'537452' b'4160' b'3736' b'1460' b'737736' b'53684' b'6276' b'683160'
b'46988' b'144' b'10892' b'1304' b'12704' b'24836' b'51752' b'65904'
b'11880' b'3488' b'49664' b'7176' b'49448' b'3060' b'42612' b'819108'
b'12436' b'6224' b'24940' b'120' b'49452' b'4016' b'3480' b'16696'
b'2232']
Feature: top_3_7_day_non_fresh_product
Tensor: [b'3056' b'1284' b'86780' b'119632' b'118652' b'2624' b'2332' b'66312'
b'2572' b'2732' b'41872' b'5736' b'1368' b'70312' b'51580' b'1312'
b'66564' b'70516' b'1232' b'25108' b'65444' b'62080' b'965524' b'5616'
b'1248' b'4220' b'66108' b'1352' b'6632' b'4816' b'1424' b'81096' b'2132'
b'NA' b'24284' b'1356' b'11200' b'8500' b'54524' b'8960' b'2776' b'2492'
b'1184' b'34580' b'5104' b'5316' b'2704' b'30292' b'12324' b'14480' b'32'
b'1228' b'88' b'3476' b'7984' b'11924' b'3460' b'78436' b'54988' b'55048'
b'1212' b'7816' b'108104' b'10896' b'2848' b'41864' b'77996' b'5388'
b'7136' b'65172' b'124' b'1500' b'9884' b'1560' b'3720' b'315932' b'7152'
b'9188' b'64596' b'8216' b'300016' b'108' b'1276' b'2192' b'2256'
b'56896' b'51504' b'24804' b'16072' b'735820' b'538028' b'2772' b'16624'
b'1596' b'51972' b'4192' b'82864' b'6348' b'19420' b'2496' b'94160'
b'67364' b'29612' b'80024' b'3808' b'9432' b'2908' b'4748' b'19740'
b'11052' b'834652' b'1344' b'583488' b'4948' b'5024' b'66476' b'64636'
b'24816' b'6364' b'74440' b'76364' b'2260' b'40896' b'8172' b'762880'
b'49664' b'1452' b'3236' b'102400' b'1360' b'1528' b'2520' b'2512'
b'2540' b'951380' b'66940' b'9956' b'57616' b'274296' b'5084' b'999344'
b'21776' b'12656' b'11220' b'3092' b'28840' b'160' b'7272' b'6032'
b'16676' b'9200' b'3060' b'14760' b'51788' b'5360' b'2180' b'40620'
b'14292' b'1268' b'12848' b'10448' b'4964' b'42488' b'17452' b'5080'
b'12176' b'139840' b'83872' b'49684' b'41524' b'767708' b'2108' b'15608'
b'8252' b'112988' b'10036' b'50848' b'6224' b'6156' b'7896' b'949800'
b'3124' b'49672' b'139636' b'76404' b'780084' b'5244' b'2584' b'4300'
b'66760' b'57084' b'4656' b'20' b'6356' b'2516' b'2680' b'19088'
b'989268' b'51672' b'5624' b'10028' b'24188' b'3260' b'1240' b'1456'
b'151936' b'51372' b'5872' b'7632' b'2412' b'2556' b'913772' b'454264'
b'813516' b'57540' b'43024' b'2688' b'6784' b'4240' b'3596' b'64056'
b'10428' b'65052' b'5664' b'66348' b'4700' b'3668' b'65836' b'1612'
b'5596' b'6292' b'682264' b'10624' b'2296' b'71500' b'2588' b'4408'
b'1244' b'6568' b'1220' b'949872' b'4704' b'3344' b'3964' b'66460'
b'3744' b'3600' b'455536' b'2568' b'293556' b'2608' b'908208' b'2208'
b'12136' b'42544' b'2144' b'3488' b'67624' b'81296' b'65096' b'5232'
b'10092' b'41544' b'67704' b'7668' b'2860' b'2244' b'64664' b'1336'
b'76708' b'67444' b'3200' b'27300' b'31068' b'8560' b'1304' b'25748'
b'12544' b'10116' b'5332' b'48276' b'33044' b'4648' b'4164' b'27072'
b'4196' b'73540' b'86924' b'28' b'287820' b'4104' b'91936' b'11872'
b'117756' b'11660' b'764728' b'79408' b'2388' b'68484' b'51660' b'1584'
b'1468' b'3144' b'6132' b'4464' b'5704' b'5076' b'56136' b'76' b'7224'
b'3816' b'36444' b'2232' b'51752' b'11836' b'60944' b'100308' b'60856'
b'99648' b'88064' b'65488' b'24912' b'592792' b'1200' b'12636' b'65452'
b'1188' b'48736' b'22320' b'65524' b'5508' b'35772' b'2488' b'191112'
b'2136' b'587804' b'7492' b'234828' b'25116' b'5312' b'561804' b'6452'
b'7436' b'48412' b'5344' b'4988' b'14524' b'2148' b'21336' b'6380'
b'6664' b'55000' b'13824' b'7720' b'68' b'55064' b'5464' b'365544'
b'9628' b'68304' b'8384' b'51292' b'5004' b'16264' b'184120' b'4112'
b'64888' b'67508' b'3228' b'65828' b'561488' b'682952' b'47852' b'66796'
b'60444' b'12204' b'67976' b'41040' b'2504' b'33276' b'75928' b'95320'
b'215712' b'65016' b'234124' b'51564' b'60940' b'16536' b'65932' b'4036'
b'8600' b'344412' b'9780' b'80216' b'65872' b'54520' b'233732' b'8592'
b'8488' b'2416' b'36504' b'84144' b'5228' b'96776' b'65824' b'10808'
b'63996' b'10348' b'21912' b'4480' b'2696' b'24916' b'9320' b'5636'
b'767868' b'12056' b'25772' b'908212' b'400992' b'23988' b'24792' b'4152'
b'7068' b'89756' b'2352' b'16652' b'946776' b'6204' b'2524' b'4328'
b'66700' b'8300' b'2348' b'82836' b'18448' b'465656' b'125324' b'7532'
b'9504' b'3660' b'40584' b'36' b'25784' b'74916' b'49756' b'955768'
b'81828' b'582564' b'1320' b'965600' b'3796' b'12716' b'601476' b'17436'
b'6848' b'22100' b'2140' b'25684' b'10104' b'42876' b'63016' b'34600'
b'55228' b'30816' b'67552' b'44412' b'6212' b'64748' b'5444' b'2152'
b'5960' b'9408' b'14764' b'20812' b'21944' b'59332' b'2864' b'3700'
b'3096' b'1572' b'55332' b'187560' b'51840' b'48408' b'9908' b'12404'
b'7108' b'3880' b'444016' b'49552' b'12360' b'4936' b'55172' b'71128'
b'44588' b'9496' b'8204' b'87196' b'86380' b'2460' b'547432' b'51444'
b'606420' b'1548' b'170900' b'108972' b'78032' b'51756' b'108812'
b'11976' b'4520' b'23408' b'75124' b'528252' b'295104' b'59624' b'12200'
b'9360' b'4172' b'60752' b'1556' b'1552' b'739472' b'6952' b'97408'
b'1224' b'4276' b'71148' b'5184' b'1324' b'4176']
Feature: top_4_7_day_non_fresh_product
Tensor: [b'51444' b'5056' b'2624' b'1500' b'88' b'55128' b'10260' b'79604'
b'65932' b'3200' b'51504' b'6380' b'5344' b'3056' b'64824' b'2412'
b'1356' b'1464' b'66460' b'1424' b'83740' b'65336' b'14760' b'1244'
b'124' b'2280' b'7080' b'300016' b'36584' b'41548' b'28' b'16696' b'NA'
b'36836' b'1452' b'4116' b'99648' b'49676' b'50876' b'12056' b'4408'
b'2416' b'62732' b'2492' b'7876' b'38164' b'5080' b'7108' b'57164'
b'51348' b'173052' b'5636' b'1232' b'1228' b'37752' b'10428' b'2556'
b'5736' b'122680' b'118652' b'97792' b'8388' b'2192' b'142952' b'3464'
b'1312' b'40360' b'65428' b'12668' b'17596' b'7736' b'4816' b'1368'
b'2572' b'11168' b'3660' b'12136' b'40412' b'7224' b'10036' b'10756'
b'682264' b'1304' b'175588' b'28368' b'2672' b'2296' b'57152' b'66564'
b'2332' b'16224' b'24788' b'5920' b'2408' b'1184' b'749048' b'1344'
b'5228' b'67508' b'4696' b'175948' b'2460' b'27208' b'1284' b'2732'
b'24836' b'85296' b'4560' b'34880' b'3124' b'914476' b'4948' b'11884'
b'3120' b'3808' b'23012' b'17756' b'5992' b'9456' b'2132' b'1276' b'1560'
b'771140' b'1240' b'6348' b'8252' b'300012' b'84720' b'38148' b'68372'
b'4196' b'14756' b'2512' b'958324' b'11664' b'97408' b'66108' b'11924'
b'5704' b'2352' b'105744' b'22308' b'3744' b'64828' b'3100' b'11224'
b'1352' b'71188' b'450748' b'90708' b'51372' b'36444' b'9344' b'66492'
b'2704' b'7984' b'6156' b'9924' b'1404' b'14524' b'6452' b'3084' b'3484'
b'55532' b'5624' b'32920' b'37916' b'1324' b'15944' b'21912' b'5688'
b'14480' b'905692' b'721788' b'16536' b'64088' b'2676' b'62252' b'8592'
b'562740' b'24896' b'52536' b'65888' b'7816' b'464592' b'8216' b'5000'
b'3392' b'3276' b'51972' b'583288' b'11944' b'15816' b'2180' b'3144'
b'5576' b'743952' b'68560' b'120872' b'949656' b'14764' b'9628' b'105900'
b'65424' b'48952' b'74916' b'11424' b'7492' b'55348' b'3196' b'2568'
b'35440' b'8920' b'3920' b'2532' b'10412' b'110196' b'84160' b'3596'
b'6072' b'2188' b'106840' b'48408' b'62636' b'8500' b'58920' b'2144'
b'462960' b'2776' b'2456' b'110144' b'15920' b'449588' b'7500' b'4800'
b'6032' b'5084' b'24' b'4520' b'2848' b'1248' b'103984' b'24912' b'6356'
b'8408' b'12096' b'41512' b'24000' b'5116' b'3108' b'12544' b'16456'
b'28356' b'3060' b'565168' b'2868' b'7436' b'1516' b'10808' b'12176'
b'68028' b'116' b'111608' b'3024' b'749300' b'15228' b'38160' b'1280'
b'11976' b'5136' b'108' b'64636' b'9504' b'66204' b'64888' b'53688'
b'50552' b'1027164' b'54044' b'56592' b'3460' b'4364' b'94812' b'196'
b'41496' b'22232' b'1220' b'7572' b'10092' b'4192' b'56468' b'19172'
b'2588' b'3616' b'11848' b'913772' b'22208' b'732280' b'4692' b'4700'
b'5444' b'2908' b'4936' b'95160' b'948188' b'953008' b'10564' b'57116'
b'489216' b'45572' b'2252' b'80880' b'74700' b'28344' b'2440' b'59300'
b'4484' b'36312' b'43528' b'118412' b'7704' b'10104' b'5776' b'68484'
b'2860' b'2584' b'6456' b'45204' b'12704' b'4172' b'912528' b'68'
b'65496' b'603544' b'36848' b'2108' b'955216' b'4488' b'36488' b'82836'
b'84652' b'42168' b'6364' b'76' b'2772' b'4924' b'78208' b'1556' b'45080'
b'1272' b'394232' b'41516' b'48600' b'10596' b'7608' b'12012' b'48704'
b'23988' b'25684' b'11200' b'66448' b'22476' b'88224' b'9408' b'64468'
b'175644' b'1212' b'63996' b'31112' b'55904' b'60440' b'25128' b'466728'
b'20264' b'20056' b'70476' b'51292' b'10896' b'54920' b'96672' b'5248'
b'12556' b'65452' b'1320' b'5508' b'730552' b'185024' b'405768' b'12436'
b'250384' b'5960' b'37652' b'9956' b'2508' b'95584' b'2348' b'490528'
b'455204' b'1528' b'114356' b'62776' b'52492' b'51756' b'10384' b'3668'
b'16236' b'81644' b'147844' b'83900' b'57024' b'1460' b'823924' b'7072'
b'6612' b'1396' b'74788' b'196688' b'2260' b'68252' b'57244' b'67688'
b'227660' b'37928' b'7832' b'66452' b'48348' b'64728' b'35956' b'10832'
b'6664' b'488608' b'3468' b'14628' b'67708' b'7084' b'16800' b'41872'
b'3600' b'6984' b'537084' b'40672' b'16144' b'211816' b'42600' b'4380'
b'8384' b'82856' b'5312' b'79356' b'54988' b'3488' b'2488' b'2552'
b'501788' b'91280' b'754876' b'962076' b'82320' b'9140' b'11488' b'10868'
b'6120' b'40844' b'164' b'40248' b'75820' b'5004' b'597460' b'2520'
b'9768' b'965604' b'9024' b'12204' b'12264' b'4356' b'63016' b'601492'
b'21672' b'84336' b'56848' b'73408' b'32904' b'42884' b'76768' b'57556'
b'48304' b'71004' b'8176' b'2256' b'22100' b'6288' b'6276' b'21276'
b'5244' b'6792' b'106456' b'6104' b'80780' b'2148' b'31068' b'4760'
b'10792' b'15652' b'3096' b'25748' b'17716' b'2112' b'187684' b'81296'
b'77960' b'10480' b'67412' b'12412' b'112040' b'2872' b'32956' b'48852'
b'71160' b'25696' b'79408' b'47464' b'16668' b'11432' b'12272' b'610392'
b'331880' b'11480' b'908208' b'2484' b'588196' b'8756' b'97980' b'5464'
b'74416' b'968940' b'454504' b'3612' b'77080' b'226108' b'73340' b'45796'
b'70428' b'115332' b'17424' b'957504' b'448748' b'17452' b'949872'
b'73232' b'10064' b'7412' b'776856' b'9908' b'12456' b'2304' b'68328'
b'2176' b'41524' b'7424' b'2688' b'9224']
Feature: top_5_7_day_non_fresh_product
Tensor: [b'88' b'6212' b'65452' b'2460' b'1356' b'NA' b'14724' b'82084' b'1352'
b'3488' b'944924' b'7272' b'100808' b'12552' b'12176' b'2572' b'2508'
b'3144' b'2188' b'681956' b'2624' b'1516' b'989736' b'1452' b'1220'
b'3596' b'4572' b'9780' b'1424' b'67000' b'86960' b'1312' b'2492'
b'64252' b'54436' b'10208' b'112' b'49680' b'48664' b'571896' b'4948'
b'2192' b'65012' b'105744' b'14760' b'44480' b'41872' b'5992' b'75124'
b'60960' b'65288' b'1284' b'2456' b'64088' b'64100' b'16160' b'10092'
b'300016' b'124' b'2608' b'19664' b'2412' b'4812' b'6348' b'2704'
b'71128' b'445108' b'31112' b'51292' b'7876' b'2772' b'12324' b'4172'
b'36828' b'92352' b'78884' b'43860' b'15608' b'11432' b'66760' b'1328'
b'3964' b'1304' b'173416' b'2688' b'78052' b'9416' b'501788' b'25128'
b'55128' b'52460' b'76' b'6816' b'3868' b'1276' b'2672' b'5232' b'67512'
b'9188' b'64596' b'1252' b'41596' b'2676' b'2540' b'3744' b'3056' b'2280'
b'2132' b'966320' b'8212' b'42484' b'1500' b'16072' b'113580' b'5080'
b'3644' b'242756' b'3736' b'3200' b'9368' b'24584' b'37652' b'41584'
b'6292' b'9472' b'2256' b'3720' b'4268' b'2556' b'6352' b'8444' b'57084'
b'193816' b'14808' b'59580' b'3880' b'37936' b'5736' b'1232' b'3660'
b'3392' b'80460' b'908208' b'146088' b'9504' b'3808' b'108104' b'4040'
b'65060' b'3468' b'51304' b'66476' b'843404' b'60440' b'55396' b'36848'
b'53688' b'74416' b'10276' b'106456' b'11024' b'8048' b'7560' b'10412'
b'3028' b'7140' b'66460' b'24228' b'8500' b'8216' b'71120' b'9956'
b'60456' b'4408' b'9884' b'65096' b'7716' b'3668' b'5988' b'54508'
b'2296' b'28376' b'65248' b'66376' b'363420' b'14732' b'557064' b'65260'
b'68500' b'64824' b'10892' b'48408' b'60856' b'14376' b'5476' b'4192'
b'12516' b'20752' b'3124' b'23176' b'12636' b'6240' b'1416' b'1228'
b'73232' b'40396' b'10756' b'406932' b'70544' b'2524' b'59840' b'173052'
b'9792' b'399060' b'67988' b'10480' b'35448' b'49164' b'3436' b'2872'
b'6612' b'65836' b'27208' b'60388' b'110200' b'761164' b'4196' b'17756'
b'2632' b'176976' b'51568' b'120872' b'9436' b'817352' b'6512' b'8768'
b'3096' b'80508' b'823924' b'3060' b'36764' b'450340' b'18152' b'4540'
b'7548' b'10616' b'3092' b'6568' b'60708' b'5184' b'8408' b'13520'
b'16732' b'1404' b'66040' b'12260' b'4104' b'115616' b'65824' b'25108'
b'86116' b'81328' b'7712' b'3516' b'764028' b'3344' b'8868' b'2440'
b'51504' b'17436' b'3700' b'1280' b'70744' b'537168' b'3564' b'80776'
b'42076' b'35520' b'2512' b'8556' b'4240' b'2588' b'107352' b'71428'
b'183896' b'88168' b'65492' b'54456' b'65828' b'12360' b'4148' b'7736'
b'5056' b'6452' b'15920' b'2516' b'250384' b'2464' b'42488' b'36836'
b'64084' b'11000' b'48412' b'77848' b'10808' b'44492' b'2732' b'57152'
b'64616' b'1560' b'5576' b'6276' b'5700' b'9324' b'4524' b'6188' b'1256'
b'64056' b'3920' b'550912' b'80536' b'70820' b'90404' b'19172' b'78308'
b'7936' b'5308' b'120' b'65428' b'2520' b'7224' b'68512' b'20000'
b'56584' b'45248' b'176164' b'26404' b'11848' b'24940' b'775664' b'5008'
b'213436' b'10420' b'7764' b'28652' b'104' b'4564' b'3476' b'3480'
b'949832' b'4960' b'51444' b'3444' b'454264' b'227660' b'6156' b'64680'
b'147844' b'42176' b'9680' b'60944' b'2776' b'21668' b'105428' b'586936'
b'36312' b'79536' b'49536' b'77892' b'70624' b'2292' b'12056' b'46036'
b'65932' b'205884' b'160' b'5704' b'44412' b'35240' b'84488' b'16696'
b'3032' b'65916' b'24816' b'54524' b'17196' b'10320' b'84016' b'5116'
b'66680' b'601476' b'51676' b'45612' b'66564' b'7424' b'89136' b'821476'
b'26628' b'12780' b'67704' b'60556' b'1324' b'2112' b'303504' b'28608'
b'68316' b'51584' b'7984' b'5228' b'102292' b'5652' b'2304' b'6132'
b'732280' b'99288' b'70368' b'70516' b'28404' b'22204' b'183168' b'8600'
b'91936' b'488608' b'2860' b'4164' b'8452' b'3108' b'10428' b'86348'
b'595828' b'9360' b'3824' b'5332' b'70332' b'78092' b'442208' b'76764'
b'9868' b'188248' b'51752' b'75160' b'76816' b'42336' b'12556' b'16544'
b'2584' b'7744' b'6204' b'33276' b'70784' b'207048' b'65860' b'22288'
b'2956' b'1224' b'40680' b'65804' b'5136' b'28348' b'65432' b'8384'
b'2108' b'4816' b'1512' b'473168' b'14480' b'11560' b'5924' b'55620'
b'956856' b'113544' b'842888' b'125220' b'34600' b'16272' b'56460'
b'1568' b'3276' b'83872' b'7072' b'7584' b'70364' b'15952' b'17452'
b'15960' b'5244' b'68560' b'27212' b'9040' b'40636' b'49448' b'947460'
b'609960' b'2348' b'64000' b'105900' b'64228' b'66108' b'57760' b'70888'
b'6068' b'92472' b'78408' b'7528' b'9756' b'41516' b'10028' b'7108'
b'11160' b'66312' b'136' b'5596' b'8064' b'68460' b'4520' b'76412'
b'62080' b'4700' b'436632' b'97408' b'3976' b'66016' b'614084' b'5032'
b'5508' b'421128' b'108296' b'74700' b'44404' b'551076' b'757168' b'5240'
b'695320' b'28700' b'66304' b'88064' b'1548' b'47592' b'26356' b'36444'
b'22356' b'450748' b'9820' b'11696' b'13884' b'7436' b'737940' b'12088'
b'573900' b'990568' b'9408' b'832488' b'147112' b'94460' b'36568'
b'74640' b'176692' b'43712' b'42392' b'960788' b'113312' b'2260' b'52000'
b'17680' b'32832' b'8896' b'103080' b'74440' b'3460' b'4160' b'12656'
b'103300' b'9600' b'5076' b'192600' b'50644' b'2708' b'105184' b'12676'
b'65688' b'36580' b'5616' b'10684']
Feature: top_1_15_day_non_fresh_product
Tensor: [b'2192' b'124' b'1352' b'1356' b'2456' b'968504' b'82084' b'2188'
b'956856' b'1528' b'736296' b'245488' b'67624' b'51504' b'2132' b'4172'
b'557064' b'88' b'57152' b'9140' b'97792' b'2496' b'64616' b'3600'
b'1312' b'1500' b'66460' b'11424' b'54436' b'10208' b'8500' b'10104'
b'1380' b'1184' b'399060' b'1276' b'4648' b'16696' b'17756' b'5344'
b'1560' b'1232' b'14764' b'2492' b'108104' b'45244' b'2572' b'771148'
b'1228' b'2412' b'60928' b'24' b'28' b'813520' b'1516' b'8592' b'2140'
b'12672' b'49552' b'44796' b'2732' b'31068' b'565672' b'1424' b'1324'
b'1452' b'4408' b'11664' b'54316' b'41548' b'809012' b'14828' b'7108'
b'64088' b'20' b'455204' b'52652' b'48688' b'7736' b'53648' b'2520'
b'3200' b'64252' b'66476' b'8388' b'53032' b'6364' b'7152' b'44480'
b'36328' b'8172' b'6348' b'25128' b'300016' b'92344' b'1284' b'8104'
b'65932' b'6564' b'25108' b'70516' b'1328' b'7668' b'764100' b'7224'
b'150304' b'6032' b'116452' b'4328' b'3808' b'1272' b'2352' b'24816'
b'3276' b'277808' b'1304' b'10808' b'12136' b'22320' b'83872' b'326180'
b'14628' b'193816' b'959932' b'3532' b'9956' b'2704' b'8252' b'65904'
b'278172' b'583488' b'2256' b'370204' b'4464' b'3976' b'3488' b'743952'
b'61112' b'10756' b'209364' b'74916' b'55348' b'3196' b'51292' b'3880'
b'4816' b'56' b'1332' b'818192' b'2260' b'4948' b'2568' b'2584' b'108'
b'4564' b'6568' b'84160' b'5348' b'12552' b'3668' b'490528' b'5672'
b'70292' b'65812' b'3092' b'84652' b'2416' b'3344' b'51448' b'41872'
b'1216' b'455536' b'447028' b'488608' b'84372' b'3236' b'5240' b'3744'
b'65428' b'66564' b'173052' b'9416' b'13680' b'147824' b'36504' b'502228'
b'2556' b'65096' b'293556' b'160' b'11632' b'33180' b'49756' b'90296'
b'3460' b'4780' b'41864' b'785820' b'22160' b'5576' b'5444' b'96600'
b'27072' b'3144' b'1512' b'216276' b'26404' b'597840' b'80536' b'2676'
b'6612' b'36568' b'95460' b'68196' b'219788' b'7980' b'5076' b'982088'
b'64228' b'37684' b'25132' b'2148' b'12556' b'16600' b'11236' b'2860'
b'164' b'3920' b'989708' b'48736' b'577000' b'77380' b'9544' b'957296'
b'11732' b'539436' b'557748' b'908208' b'77908' b'4160' b'5080' b'168'
b'581988' b'592792' b'103520' b'8384' b'63120' b'84' b'10896' b'89136'
b'72' b'24980' b'18196' b'4812' b'3056' b'60944' b'2508' b'9884' b'3484'
b'83740' b'3720' b'501784' b'28200' b'776584' b'1240' b'14480' b'1404'
b'4192' b'6212' b'2280' b'64824' b'12204' b'24836' b'1456' b'1360'
b'1548' b'9344' b'113520' b'12532' b'10428' b'65828' b'12176' b'54976'
b'11944' b'2112' b'6664' b'6156' b'68304' b'142452' b'5636' b'57148'
b'1224' b'3564' b'65916' b'5136' b'5688' b'42600' b'5616' b'2208'
b'51556' b'12544' b'114072' b'16748' b'17528' b'12184' b'66796' b'11560'
b'119164' b'91696' b'6104' b'51716' b'11488' b'597344' b'147572' b'12436'
b'1252' b'3124' b'1368' b'52220' b'7584' b'1268' b'7492' b'95916'
b'92164' b'32' b'65012' b'42912' b'73408' b'17596' b'76' b'77936'
b'18948' b'68' b'44412' b'989268' b'9756' b'4104' b'373576' b'121212'
b'753992' b'99648' b'1212' b'455356' b'44860' b'9012' b'7712' b'2268'
b'379780' b'3028' b'5404' b'2304' b'24112' b'5736' b'11848' b'65696'
b'97332' b'7896' b'444016' b'10424' b'12360' b'33276' b'6452' b'414468'
b'71128' b'87020' b'2296' b'54988' b'454264' b'6072' b'9040' b'573900'
b'1460' b'96944' b'2288' b'2264' b'83596' b'64112' b'60904' b'4520'
b'6524' b'45796' b'3516' b'448748' b'782776' b'55156' b'37752' b'49448'
b'8808' b'819108' b'809428' b'4560' b'6224' b'5236' b'48408' b'3736'
b'65288' b'306644' b'10320' b'2532' b'7424' b'2244']
Feature: top_2_15_day_non_fresh_product
Tensor: [b'51504' b'51716' b'65436' b'1352' b'1228' b'2492' b'1356' b'41552'
b'5844' b'1764' b'4240' b'10104' b'1312' b'609960' b'124' b'66564'
b'70516' b'2188' b'66460' b'7668' b'62080' b'1424' b'88' b'8500' b'1284'
b'1332' b'835436' b'60904' b'81096' b'1500' b'12556' b'80460' b'4816'
b'2572' b'12176' b'8960' b'6348' b'25108' b'2416' b'3668' b'41872'
b'14756' b'37752' b'28' b'4196' b'300012' b'1560' b'3460' b'97792'
b'173436' b'36824' b'1184' b'142952' b'5988' b'40456' b'1276' b'74704'
b'31112' b'60856' b'65828' b'2860' b'2584' b'3720' b'66088' b'2280'
b'5240' b'64596' b'10756' b'300016' b'12204' b'482992' b'3200' b'2296'
b'44872' b'503952' b'24804' b'565592' b'65688' b'3344' b'78672' b'16624'
b'2676' b'7152' b'70784' b'2456' b'82864' b'19664' b'2132' b'1620'
b'65052' b'3092' b'2192' b'4748' b'6612' b'118652' b'210984' b'5992'
b'67412' b'57152' b'32' b'3056' b'2352' b'1240' b'4692' b'66476' b'16600'
b'1304' b'917780' b'40896' b'75152' b'6452' b'2516' b'2440' b'102400'
b'24' b'4656' b'2412' b'3744' b'51672' b'8560' b'70340' b'7108' b'965524'
b'31068' b'10792' b'36568' b'989584' b'10276' b'453164' b'8592' b'414180'
b'3124' b'16676' b'4520' b'10036' b'70444' b'9040' b'135372' b'50876'
b'1324' b'20' b'4524' b'49712' b'160' b'5688' b'50552' b'45572' b'583488'
b'2704' b'455536' b'4408' b'49732' b'348700' b'6356' b'2732' b'2176'
b'4480' b'71452' b'139872' b'14764' b'4276' b'5616' b'5344' b'68560'
b'2488' b'6364' b'1344' b'766544' b'64088' b'173052' b'1368' b'399060'
b'455204' b'68512' b'3920' b'9036' b'105632' b'1452' b'NA' b'50636'
b'2256' b'454264' b'36444' b'73076' b'54988' b'4172' b'67028' b'82364'
b'76200' b'2776' b'907328' b'48664' b'2608' b'449588' b'5664' b'65428'
b'5388' b'6352' b'65836' b'1468' b'5596' b'89688' b'51444' b'24912'
b'81596' b'11336' b'2140' b'957420' b'8624' b'3196' b'4704' b'73268'
b'227156' b'1232' b'8556' b'90236' b'1536' b'1456' b'66796' b'68028'
b'4924' b'2688' b'3564' b'2144' b'115876' b'65932' b'87612' b'1420'
b'17756' b'36852' b'12552' b'65708' b'73244' b'5736' b'2508' b'33412'
b'49760' b'5432' b'11924' b'1220' b'5080' b'2552' b'565772' b'10808'
b'6132' b'2540' b'59620' b'33044' b'3276' b'37552' b'18448' b'43932'
b'54044' b'67552' b'766024' b'11512' b'153472' b'5960' b'6568' b'8064'
b'3096' b'475956' b'2304' b'65816' b'66568' b'44480' b'5704' b'74684'
b'61064' b'2848' b'6052' b'51292' b'3488' b'24940' b'71428' b'2672'
b'2388' b'11344' b'123432' b'27256' b'1584' b'4564' b'816152' b'2240'
b'7224' b'12848' b'592792' b'9188' b'450356' b'1244' b'8388' b'12360'
b'1360' b'7716' b'10892' b'10200' b'45080' b'82604' b'83740' b'444016'
b'561804' b'7492' b'57148' b'4040' b'48704' b'184120' b'28376' b'14524'
b'1436' b'16696' b'3468' b'3784' b'5508' b'8424' b'365544' b'8384'
b'103884' b'2556' b'251808' b'58312' b'36848' b'24984' b'97376' b'9956'
b'35988' b'41040' b'10428' b'67976' b'5228' b'2512' b'2504' b'33276'
b'7984' b'145892' b'64824' b'63996' b'6512' b'776524' b'51756' b'15920'
b'16536' b'3596' b'530892' b'2484' b'10384' b'43976' b'71004' b'80656'
b'6748' b'3060' b'64096' b'48600' b'233732' b'25116' b'108' b'3144'
b'19740' b'765808' b'82836' b'89560' b'62252' b'251884' b'2168' b'10224'
b'16236' b'5444' b'12544' b'7080' b'7856' b'10184' b'970944' b'7744'
b'2696' b'11468' b'24916' b'51372' b'83132' b'3868' b'6380' b'6100'
b'1256' b'964104' b'65424' b'1516' b'17104' b'68460' b'66348' b'3028'
b'3324' b'1552' b'1280' b'17596' b'214408' b'5672' b'5232' b'8644'
b'182520' b'85456' b'1320' b'65968' b'903488' b'11848' b'601476' b'3284'
b'3736' b'70292' b'66620' b'9276' b'746308' b'55440' b'965600' b'4380'
b'59580' b'48264' b'1404' b'375392' b'60444' b'6968' b'5576' b'7228'
b'67244' b'214472' b'286488' b'84356' b'4948' b'17468' b'1272' b'13636'
b'11424' b'10028' b'613884' b'612080' b'144788' b'59332' b'9224'
b'193264' b'11432' b'64664' b'9408' b'24788' b'24116' b'6032' b'1572'
b'5676' b'203036' b'74916' b'6816' b'24640' b'16' b'724144' b'8124'
b'3956' b'65812' b'8920' b'200112' b'273364' b'51284' b'749048' b'4160'
b'24932' b'170900' b'737736' b'9132' b'6276' b'683160' b'48556' b'957816'
b'502356' b'24836' b'4956' b'65904' b'31116' b'2260' b'49664' b'234984'
b'11052' b'20984' b'913732' b'12436' b'1224' b'557344' b'91612' b'4812'
b'4016' b'107352' b'14584' b'2112']
Feature: top_3_15_day_non_fresh_product
Tensor: [b'88' b'1284' b'65452' b'119632' b'118652' b'2624' b'1352' b'76' b'2572'
b'7424' b'3488' b'66088' b'70312' b'51580' b'1312' b'2508' b'65932'
b'1228' b'94472' b'65444' b'59608' b'66564' b'65336' b'14760' b'1244'
b'5616' b'2732' b'1620' b'66108' b'1424' b'108948' b'1356' b'1232'
b'2488' b'949352' b'2556' b'11200' b'8500' b'54524' b'455536' b'2132'
b'19740' b'62732' b'1368' b'5316' b'14764' b'2704' b'173052' b'32'
b'9376' b'6452' b'590676' b'5312' b'65428' b'4564' b'78436' b'55048'
b'4948' b'3108' b'114296' b'9344' b'6348' b'41864' b'77996' b'24816'
b'1512' b'6844' b'1184' b'3200' b'124' b'1500' b'11168' b'1560' b'1404'
b'19068' b'3056' b'67216' b'5080' b'11432' b'293556' b'16132' b'86780'
b'5812' b'2688' b'56896' b'16800' b'NA' b'6816' b'538028' b'2772' b'3736'
b'61220' b'51784' b'27296' b'331880' b'1240' b'64596' b'7224' b'2492'
b'3744' b'51504' b'1220' b'1452' b'300016' b'80024' b'550912' b'9432'
b'36584' b'82836' b'7720' b'1344' b'583488' b'24' b'4696' b'300012'
b'64636' b'57084' b'60856' b'41872' b'56592' b'59580' b'25136' b'74700'
b'57152' b'42424' b'2348' b'1276' b'94160' b'146088' b'749928' b'10296'
b'2520' b'56472' b'1304' b'11224' b'24836' b'2140' b'3144' b'68496'
b'3392' b'51444' b'73276' b'12336' b'2148' b'160' b'548824' b'171184'
b'10412' b'7108' b'399060' b'5100' b'28' b'596104' b'40456' b'1332'
b'2192' b'81096' b'32920' b'57116' b'4964' b'10036' b'9036' b'68484'
b'613884' b'49684' b'25132' b'8644' b'51372' b'4520' b'14732' b'112988'
b'3336' b'14524' b'2332' b'2208' b'2172' b'7896' b'78876' b'4408' b'3124'
b'5184' b'76404' b'5244' b'2584' b'7492' b'64680' b'31068' b'909888'
b'2456' b'4572' b'989268' b'5624' b'964960' b'45440' b'76808' b'65892'
b'151936' b'20' b'5872' b'786164' b'761164' b'4816' b'454264' b'4000'
b'43024' b'9436' b'3564' b'2392' b'3084' b'68304' b'2412' b'52004'
b'7712' b'10428' b'450340' b'7500' b'65436' b'6568' b'7608' b'5084'
b'12324' b'1612' b'5596' b'682264' b'82464' b'82244' b'11800' b'2588'
b'70516' b'83740' b'15840' b'4704' b'948284' b'97792' b'2676' b'8868'
b'12656' b'2568' b'328828' b'66460' b'75124' b'25772' b'9544' b'2608'
b'7736' b'908208' b'1248' b'3476' b'11768' b'4700' b'32856' b'76588'
b'104868' b'5028' b'10092' b'2516' b'7668' b'2244' b'64664' b'2672'
b'54988' b'10992' b'67444' b'3480' b'10064' b'4016' b'6632' b'4196'
b'80460' b'64952' b'4148' b'2872' b'488872' b'8216' b'5344' b'557064'
b'16668' b'6156' b'64088' b'16536' b'45612' b'4936' b'66448' b'91936'
b'11872' b'8552' b'3060' b'764728' b'2388' b'1468' b'120' b'59300'
b'43528' b'908244' b'12176' b'555808' b'20152' b'34380' b'2352' b'24940'
b'64004' b'557344' b'3596' b'2540' b'3092' b'105900' b'71296' b'10956'
b'24912' b'2108' b'603544' b'12056' b'12636' b'1188' b'2860' b'8252'
b'2256' b'5508' b'16176' b'60944' b'191112' b'2136' b'14532' b'7804'
b'11708' b'84' b'1224' b'586936' b'9956' b'957176' b'5736' b'7436'
b'11664' b'62860' b'21336' b'9408' b'12580' b'949668' b'168' b'5116'
b'22916' b'3920' b'16264' b'4104' b'184120' b'12556' b'64888' b'67508'
b'193816' b'3228' b'42608' b'37916' b'988636' b'47852' b'5388' b'405768'
b'577092' b'5236' b'3600' b'108' b'83872' b'95320' b'71440' b'540040'
b'489120' b'1456' b'66332' b'2848' b'961820' b'4036' b'98160' b'20880'
b'110032' b'11924' b'145892' b'65260' b'57024' b'1212' b'8592' b'48600'
b'4240' b'65836' b'84160' b'17756' b'5704' b'6132' b'36444' b'75696'
b'6628' b'4248' b'51752' b'10808' b'9504' b'67688' b'10348' b'6664'
b'488608' b'5944' b'8384' b'10892' b'33276' b'5636' b'908212' b'400992'
b'7632' b'2912' b'52000' b'51716' b'2296' b'7068' b'180204' b'12544'
b'916296' b'16652' b'108712' b'7880' b'5476' b'14480' b'205884' b'25696'
b'43108' b'8300' b'7168' b'9320' b'48608' b'54976' b'43384' b'770864'
b'4328' b'24784' b'82364' b'962268' b'51448' b'36' b'25784' b'49756'
b'83460' b'66960' b'2524' b'74436' b'4608' b'1320' b'2152' b'65664'
b'965604' b'3460' b'3700' b'3260' b'56848' b'496468' b'36912' b'4172'
b'52672' b'42876' b'121728' b'40864' b'71004' b'3668' b'25108' b'551076'
b'25128' b'777220' b'5444' b'2212' b'1384' b'64748' b'54980' b'54436'
b'6792' b'750428' b'2180' b'5984' b'36328' b'9788' b'15652' b'4192'
b'8080' b'3096' b'1420' b'55332' b'64100' b'443368' b'51840' b'48408'
b'66304' b'26544' b'12404' b'5240' b'35584' b'44588' b'36824' b'8204'
b'16600' b'5576' b'966360' b'2460' b'113336' b'828748' b'51348' b'9188'
b'2696' b'108972' b'53684' b'46988' b'413740' b'679560' b'90296' b'49712'
b'5004' b'528252' b'9360' b'42612' b'111148' b'739472' b'97408' b'12456'
b'183240' b'45712' b'51868' b'1328' b'1324' b'2232' b'4176']
Feature: top_4_15_day_non_fresh_product
Tensor: [b'1284' b'2572' b'2624' b'124' b'1356' b'1424' b'31068' b'1232' b'65932'
b'64888' b'3484' b'5736' b'5344' b'65060' b'64824' b'2412' b'88' b'1452'
b'537340' b'66476' b'17756' b'1220' b'211816' b'12556' b'40456' b'2192'
b'300016' b'66460' b'3460' b'4816' b'2492' b'64748' b'5464' b'1560'
b'565660' b'2608' b'99648' b'54980' b'50876' b'1276' b'65140' b'68484'
b'7108' b'38164' b'5992' b'3428' b'12324' b'1352' b'3344' b'61448'
b'1228' b'7984' b'11924' b'122680' b'24' b'63120' b'3744' b'24816'
b'10896' b'79752' b'1312' b'2860' b'65436' b'2144' b'4148' b'7136'
b'65172' b'108104' b'9884' b'40412' b'140912' b'3880' b'7152' b'48736'
b'4708' b'211568' b'453788' b'9040' b'16600' b'57152' b'98044' b'14732'
b'1240' b'19740' b'NA' b'13860' b'2408' b'4948' b'66348' b'70888'
b'64088' b'6276' b'1332' b'34664' b'53032' b'19420' b'2496' b'3056'
b'12176' b'4284' b'450748' b'4648' b'3124' b'914476' b'11804' b'9672'
b'7528' b'10428' b'313612' b'9308' b'7336' b'834652' b'9456' b'2256'
b'14584' b'771140' b'4748' b'1436' b'74440' b'544116' b'1256' b'66088'
b'76364' b'2260' b'4464' b'62080' b'3092' b'5136' b'100292' b'2416'
b'1360' b'11468' b'12204' b'22308' b'2180' b'2332' b'7424' b'4172'
b'3824' b'170036' b'57616' b'274296' b'5084' b'1500' b'12656' b'2552'
b'28840' b'2132' b'4104' b'56' b'14760' b'51788' b'2140' b'2540'
b'915816' b'40620' b'356280' b'573728' b'65096' b'84720' b'1456' b'5080'
b'9376' b'16236' b'71428' b'34640' b'28' b'1324' b'12544' b'41524'
b'8680' b'63016' b'15608' b'562740' b'5132' b'52536' b'10104' b'3200'
b'464592' b'60856' b'1556' b'496796' b'3276' b'8388' b'756752' b'36444'
b'6348' b'3144' b'108' b'66760' b'64636' b'2504' b'10488' b'4656'
b'908208' b'3596' b'2516' b'19172' b'9384' b'41552' b'11424' b'7492'
b'8500' b'8880' b'588416' b'3468' b'93884' b'3392' b'9956' b'331880'
b'51504' b'2352' b'65016' b'5116' b'56460' b'48408' b'2704' b'10724'
b'1184' b'4240' b'3644' b'1212' b'342536' b'52316' b'7980' b'12096'
b'3060' b'2508' b'9140' b'966360' b'20' b'2848' b'45124' b'1572' b'6292'
b'10624' b'150100' b'6568' b'38452' b'5228' b'4936' b'1244' b'42084'
b'2772' b'4564' b'3964' b'7632' b'2868' b'5232' b'8868' b'1516' b'293556'
b'71484' b'4192' b'8552' b'436864' b'66564' b'4408' b'43196' b'11976'
b'5616' b'51292' b'3488' b'9504' b'7224' b'53688' b'11024' b'14480'
b'4364' b'2676' b'61112' b'557344' b'56076' b'955216' b'11220' b'7572'
b'5248' b'610036' b'10792' b'6820' b'764628' b'68204' b'26508' b'90340'
b'2732' b'5332' b'965524' b'24980' b'5636' b'51716' b'16544' b'765204'
b'2908' b'7176' b'24836' b'73540' b'86924' b'2348' b'64056' b'2212'
b'57116' b'160' b'21152' b'70820' b'74700' b'51660' b'1584' b'5688'
b'14828' b'68512' b'4380' b'823960' b'44416' b'51484' b'51304' b'3236'
b'11664' b'2588' b'172' b'51372' b'4036' b'100308' b'6356' b'10036'
b'3480' b'20180' b'88064' b'44992' b'65428' b'2208' b'57644' b'2512'
b'16128' b'25132' b'2108' b'3028' b'6156' b'16696' b'82836' b'65012'
b'762820' b'2188' b'4924' b'41512' b'51424' b'14500' b'587804' b'244148'
b'5312' b'6212' b'8592' b'6452' b'12012' b'749048' b'4988' b'2900'
b'8384' b'22476' b'9872' b'48900' b'6664' b'17436' b'955756' b'8920'
b'2568' b'7668' b'79408' b'28888' b'2440' b'8768' b'54232' b'4196'
b'8300' b'908236' b'8644' b'54920' b'3600' b'16456' b'65452' b'92284'
b'2456' b'5508' b'561488' b'44884' b'185024' b'65836' b'12532' b'956556'
b'66956' b'6132' b'41040' b'51584' b'66952' b'75928' b'95584' b'215712'
b'759628' b'581988' b'4520' b'79924' b'136' b'11200' b'87020' b'1404'
b'65664' b'169984' b'16168' b'488608' b'76144' b'36568' b'80948'
b'121688' b'25128' b'47936' b'7072' b'551012' b'74788' b'1464' b'56624'
b'2384' b'38056' b'8348' b'4508' b'514072' b'25772' b'32' b'10832'
b'1328' b'682264' b'786928' b'49180' b'67708' b'7084' b'6668' b'16800'
b'777268' b'537084' b'23988' b'12456' b'11428' b'55904' b'13632' b'16676'
b'50668' b'408220' b'24284' b'329244' b'6204' b'2524' b'57148' b'3524'
b'26808' b'5704' b'48424' b'54988' b'12112' b'42460' b'33276' b'11688'
b'45204' b'48624' b'78848' b'60944' b'65972' b'3660' b'577232' b'2460'
b'83740' b'5592' b'70516' b'164' b'3808' b'5056' b'86140' b'66992'
b'4700' b'81828' b'60436' b'2872' b'10808' b'3668' b'12264' b'2296'
b'22100' b'4276' b'79384' b'42884' b'946780' b'24788' b'12380' b'73248'
b'22532' b'31060' b'15920' b'756964' b'430392' b'7532' b'3108' b'2248'
b'5944' b'10224' b'2148' b'4760' b'21944' b'809012' b'33920' b'16136'
b'40360' b'84928' b'82364' b'81296' b'77960' b'234900' b'51448' b'67412'
b'12412' b'48852' b'70648' b'4880' b'9368' b'47464' b'12272' b'87196'
b'65812' b'731448' b'65488' b'86380' b'7608' b'2484' b'537452' b'97980'
b'606420' b'454504' b'78032' b'21704' b'108812' b'68' b'91936' b'149236'
b'5008' b'957504' b'17452' b'66684' b'4328' b'68500' b'32836' b'7412'
b'4696' b'60752' b'1552' b'776856' b'1344' b'485780' b'6364' b'49452'
b'363512' b'2176' b'71148' b'49708' b'9224']
Feature: top_5_15_day_non_fresh_product
Tensor: [b'2732' b'5056' b'1424' b'1500' b'124' b'63116' b'2456' b'1352' b'2192'
b'5316' b'6380' b'1368' b'12552' b'2332' b'88' b'22260' b'8500' b'60940'
b'1600' b'1212' b'36488' b'1452' b'2144' b'965524' b'3596' b'19124'
b'2488' b'300012' b'6632' b'5344' b'2624' b'2516' b'25128' b'2412'
b'565772' b'66564' b'4116' b'16536' b'544116' b'23992' b'2304' b'2492'
b'34580' b'99648' b'41872' b'1284' b'75124' b'30292' b'51348' b'7668'
b'5636' b'1276' b'66760' b'544756' b'1332' b'2508' b'76220' b'3824'
b'83740' b'18468' b'2584' b'2704' b'40360' b'445108' b'5616' b'5388'
b'7736' b'4816' b'2772' b'9792' b'8960' b'3084' b'149492' b'7224'
b'10036' b'1360' b'682264' b'530580' b'1232' b'2388' b'16524' b'50876'
b'78052' b'9416' b'36764' b'16072' b'1404' b'NA' b'21108' b'116796'
b'4908' b'749048' b'28' b'96324' b'1588' b'998260' b'1620' b'12136'
b'175948' b'81596' b'6348' b'79408' b'2296' b'62448' b'24836' b'94160'
b'4564' b'2132' b'3056' b'7744' b'1612' b'11884' b'86400' b'2676'
b'106456' b'3276' b'10092' b'121208' b'949352' b'9472' b'3096' b'51292'
b'4268' b'559092' b'7228' b'108' b'5024' b'1560' b'1280' b'24816'
b'64596' b'2776' b'3124' b'2512' b'4328' b'8808' b'83900' b'5812'
b'829024' b'3236' b'54436' b'16160' b'5704' b'51304' b'76816' b'2540'
b'153888' b'64228' b'6156' b'65932' b'4196' b'71188' b'450748' b'9544'
b'999344' b'2800' b'989708' b'211816' b'7716' b'3092' b'66492' b'1356'
b'4220' b'2696' b'5084' b'68' b'3532' b'3744' b'49532' b'24584' b'64748'
b'55532' b'2568' b'20' b'4408' b'46264' b'25108' b'61696' b'15980'
b'17452' b'809012' b'497400' b'51504' b'1220' b'64088' b'16680' b'68196'
b'62252' b'3668' b'65260' b'49760' b'2860' b'4160' b'1312' b'949800'
b'8216' b'3488' b'3392' b'3460' b'28840' b'1324' b'12516' b'11696'
b'7984' b'11756' b'3772' b'562128' b'3060' b'120872' b'27208' b'3100'
b'4192' b'8212' b'70544' b'11444' b'56624' b'3144' b'1184' b'348820'
b'8920' b'17896' b'5080' b'24' b'10412' b'36504' b'110196' b'7108'
b'15960' b'25124' b'5292' b'813516' b'8388' b'51568' b'12324' b'2572'
b'1556' b'6784' b'2688' b'19740' b'2108' b'595828' b'1188' b'49676'
b'15920' b'1464' b'4364' b'65436' b'10792' b'4700' b'92264' b'3808'
b'51444' b'1248' b'65628' b'2352' b'35548' b'2140' b'3332' b'12540'
b'58356' b'13520' b'6356' b'66040' b'947664' b'203448' b'65252' b'10896'
b'949872' b'24000' b'3108' b'16456' b'12176' b'565168' b'3344' b'331880'
b'45440' b'2440' b'3600' b'360296' b'3700' b'116' b'51752' b'5100'
b'34608' b'749300' b'32' b'2256' b'64004' b'4104' b'25696' b'8556'
b'597420' b'17756' b'3268' b'12272' b'2588' b'58892' b'2496' b'14188'
b'10048' b'193816' b'3720' b'68560' b'71340' b'94812' b'71500' b'18504'
b'22232' b'9884' b'8560' b'1272' b'25748' b'12544' b'780480' b'104'
b'583820' b'11848' b'1516' b'48276' b'2460' b'3200' b'184120' b'2848'
b'1320' b'2244' b'4524' b'41524' b'95160' b'65012' b'344412' b'6968'
b'3880' b'74704' b'25692' b'10428' b'19172' b'78308' b'55064' b'6212'
b'1420' b'34716' b'2520' b'4520' b'84160' b'4484' b'5136' b'20236' b'76'
b'4812' b'14760' b'64136' b'65836' b'32936' b'64096' b'2112' b'11836'
b'60944' b'10072' b'110704' b'113896' b'14524' b'4172' b'14564' b'3480'
b'22160' b'56804' b'51864' b'66476' b'3444' b'36836' b'65452' b'22320'
b'277808' b'36824' b'250336' b'357996' b'777220' b'60928' b'6276' b'1456'
b'56584' b'119344' b'45708' b'1240' b'788508' b'48600' b'219652' b'7084'
b'70396' b'7608' b'46036' b'5736' b'23988' b'3736' b'784484' b'9200'
b'6568' b'11656' b'44412' b'64056' b'64468' b'55000' b'13824' b'7720'
b'12444' b'42604' b'51372' b'9956' b'7980' b'64828' b'5596' b'8408'
b'60440' b'58884' b'4292' b'96672' b'51448' b'65948' b'65892' b'12832'
b'730552' b'52004' b'6132' b'300016' b'56924' b'970360' b'67976' b'16224'
b'45204' b'66348' b'7896' b'2164' b'1416' b'102292' b'464592' b'761392'
b'51716' b'785236' b'1528' b'6664' b'52492' b'2348' b'5988' b'757252'
b'82348' b'1304' b'80216' b'1396' b'7892' b'10892' b'784884' b'1460'
b'823924' b'8452' b'2868' b'2876' b'86348' b'15808' b'36568' b'5332'
b'67196' b'4784' b'8956' b'533500' b'63996' b'4964' b'12556' b'16544'
b'65916' b'76868' b'32960' b'7584' b'25772' b'2148' b'6984' b'7424'
b'12460' b'55908' b'989584' b'16144' b'10196' b'501784' b'3868' b'4380'
b'8384' b'946776' b'47016' b'476188' b'908208' b'4704' b'66452' b'5924'
b'66700' b'65688' b'2552' b'82836' b'582000' b'14608' b'293556' b'125324'
b'63380' b'7492' b'1328' b'3784' b'99288' b'40584' b'5992' b'6452'
b'75820' b'955768' b'597460' b'538028' b'3464' b'965648' b'54360' b'5244'
b'4240' b'8080' b'25684' b'86444' b'947460' b'12656' b'488608' b'64000'
b'76768' b'66108' b'42636' b'93044' b'30816' b'35028' b'1228' b'87016'
b'777928' b'12636' b'11160' b'9408' b'4480' b'42484' b'14764' b'5676'
b'19932' b'8064' b'9140' b'68460' b'56592' b'40396' b'98136' b'2864'
b'17716' b'66016' b'187560' b'421128' b'108296' b'185700' b'74700'
b'44404' b'60856' b'2292' b'49552' b'11000' b'1548' b'47592' b'11664'
b'7704' b'22356' b'76920' b'10208' b'500080' b'13884' b'7436' b'14724'
b'582564' b'5464' b'172188' b'226108' b'51580' b'144' b'5132' b'2248'
b'11560' b'5872' b'42392' b'295104' b'3544' b'67244' b'54324' b'56600'
b'8896' b'65488' b'557344' b'2188' b'9908' b'40' b'31068' b'11592'
b'147232' b'913992' b'2708' b'10480' b'6612' b'184704' b'10684']
Feature: top_1_60_day_non_fresh_product
Tensor: [b'24980' b'124' b'1352' b'1356' b'2456' b'82084' b'65932' b'956856'
b'53676' b'10104' b'245488' b'2572' b'2192' b'51504' b'2188' b'3440'
b'66460' b'88' b'65336' b'8500' b'1284' b'392420' b'2496' b'64616'
b'835436' b'1312' b'1500' b'54436' b'4116' b'40908' b'1424' b'14796'
b'1184' b'399060' b'99648' b'2704' b'17756' b'5344' b'1560' b'4196'
b'65428' b'2492' b'91936' b'7488' b'45244' b'40456' b'77996' b'771148'
b'1228' b'2412' b'24' b'2584' b'40412' b'813520' b'5080' b'66760' b'8592'
b'2140' b'60396' b'64088' b'12552' b'2732' b'31068' b'28' b'3488' b'1452'
b'4408' b'1276' b'25128' b'755440' b'7500' b'66320' b'14828' b'7108'
b'66376' b'57152' b'20' b'455204' b'9904' b'48688' b'7736' b'64252'
b'66476' b'8388' b'1232' b'8444' b'74700' b'36328' b'8172' b'6348'
b'92344' b'7224' b'782860' b'70516' b'274296' b'19740' b'10276' b'602856'
b'234900' b'116452' b'73268' b'3808' b'1272' b'10756' b'3276' b'76'
b'10808' b'6844' b'613884' b'326180' b'193816' b'51372' b'9956' b'8252'
b'65904' b'10792' b'14764' b'11160' b'61112' b'65424' b'9384' b'12176'
b'173052' b'2132' b'3196' b'51292' b'682264' b'750000' b'4816' b'36504'
b'1332' b'538028' b'7632' b'454264' b'64280' b'4564' b'2688' b'2256'
b'2776' b'45568' b'7608' b'3668' b'490528' b'51444' b'82464' b'3200'
b'70292' b'66252' b'84652' b'2416' b'51448' b'5704' b'488608' b'5240'
b'66564' b'3476' b'9416' b'87612' b'147824' b'82996' b'502228' b'2556'
b'10896' b'73244' b'2516' b'14480' b'293556' b'2860' b'64664' b'36568'
b'955216' b'90296' b'83900' b'25108' b'4016' b'60856' b'908212' b'59608'
b'746452' b'2460' b'5444' b'3744' b'96600' b'7176' b'14628' b'216276'
b'65244' b'84372' b'87016' b'4704' b'6612' b'95460' b'68196' b'20000'
b'5076' b'25132' b'12556' b'24940' b'105900' b'949656' b'750380'
b'603544' b'48704' b'78672' b'146784' b'65836' b'11732' b'539436' b'1240'
b'913728' b'25116' b'908208' b'41872' b'77908' b'4160' b'48736' b'300016'
b'28376' b'581988' b'14524' b'64056' b'48900' b'16696' b'8384' b'68392'
b'13824' b'2568' b'365544' b'111260' b'89136' b'2452' b'25136' b'52004'
b'588196' b'2508' b'16116' b'9884' b'3484' b'83740' b'3720' b'501784'
b'64824' b'76588' b'215712' b'540040' b'2540' b'4192' b'6212' b'91720'
b'110032' b'11924' b'404856' b'1456' b'1360' b'16456' b'60940' b'233732'
b'71428' b'11944' b'442208' b'2112' b'6664' b'6156' b'142452' b'66148'
b'3144' b'7984' b'144768' b'24836' b'598956' b'567196' b'32' b'5688'
b'959932' b'5616' b'51556' b'12544' b'10036' b'16748' b'66348' b'3028'
b'12184' b'66796' b'11560' b'17596' b'91696' b'2144' b'3344' b'51716'
b'597344' b'955168' b'6568' b'11848' b'538184' b'49756' b'62916' b'4700'
b'1304' b'746308' b'65664' b'78052' b'7492' b'92164' b'966464' b'60444'
b'12380' b'108' b'67244' b'9544' b'77936' b'18948' b'4948' b'2208'
b'76396' b'5324' b'85348' b'24816' b'12056' b'10028' b'586936' b'121212'
b'7712' b'2248' b'574696' b'5404' b'7572' b'982568' b'65696' b'444016'
b'16' b'761164' b'33276' b'6452' b'2280' b'6364' b'65812' b'56024'
b'65488' b'583488' b'6072' b'12540' b'9040' b'96944' b'1380' b'2264'
b'83596' b'64112' b'60904' b'7152' b'3516' b'3828' b'448748' b'782776'
b'55156' b'75788' b'49448' b'4172' b'331880' b'4560' b'97408' b'183240'
b'3736' b'65288' b'306644' b'55240' b'98496']
Feature: top_2_60_day_non_fresh_product
Tensor: [b'24984' b'2572' b'65436' b'1352' b'1228' b'2492' b'508720' b'48736'
b'2188' b'64632' b'2144' b'59624' b'1500' b'5344' b'1312' b'64824'
b'52004' b'66564' b'70516' b'94472' b'1356' b'557064' b'36488' b'57152'
b'1424' b'2488' b'88' b'9140' b'909192' b'7712' b'6668' b'12556' b'81596'
b'65428' b'11200' b'54524' b'455536' b'65140' b'25128' b'10756' b'3668'
b'66460' b'14796' b'14756' b'8888' b'24836' b'80020' b'4240' b'28'
b'590676' b'300012' b'65812' b'4564' b'97792' b'63120' b'1560' b'24816'
b'142952' b'5988' b'65444' b'945984' b'7736' b'60928' b'6664' b'8500'
b'3056' b'67216' b'124' b'2280' b'5240' b'64596' b'73268' b'530580'
b'1232' b'9040' b'2688' b'83900' b'2192' b'16800' b'565672' b'3200'
b'538028' b'1284' b'8444' b'65016' b'5228' b'78180' b'12176' b'1452'
b'809012' b'2132' b'64616' b'27072' b'2456' b'80024' b'914476' b'67328'
b'76' b'6612' b'51740' b'118652' b'10092' b'75352' b'949352' b'2352'
b'24' b'12456' b'82836' b'6364' b'7152' b'51504' b'44480' b'917780'
b'1276' b'65932' b'762880' b'2520' b'908208' b'2440' b'102400' b'10296'
b'300016' b'51292' b'8388' b'8104' b'4192' b'11224' b'66476' b'965524'
b'2556' b'452164' b'10808' b'7668' b'989584' b'73276' b'738512' b'6032'
b'16676' b'11084' b'10792' b'3144' b'2540' b'32920' b'1324' b'65096'
b'4572' b'73244' b'16236' b'5688' b'22320' b'83872' b'14628' b'66760'
b'9416' b'5736' b'45644' b'8592' b'14524' b'603544' b'3460' b'5184'
b'76404' b'139872' b'2508' b'5080' b'4656' b'10036' b'6356' b'49756'
b'64088' b'1368' b'4816' b'455204' b'25716' b'6348' b'151936' b'56'
b'183996' b'91936' b'4948' b'454264' b'2632' b'2460' b'6452' b'65260'
b'6132' b'67028' b'2584' b'66376' b'41864' b'76200' b'9956' b'2412'
b'102272' b'61112' b'449588' b'5664' b'5084' b'6352' b'908236' b'36568'
b'2140' b'12540' b'71500' b'20' b'957420' b'54584' b'1184' b'3196'
b'4704' b'2304' b'3124' b'16456' b'41872' b'11940' b'3744' b'4408'
b'68028' b'84372' b'3236' b'65836' b'119632' b'2256' b'51672' b'65708'
b'31116' b'36312' b'309828' b'12600' b'25108' b'2512' b'160' b'24988'
b'11632' b'5312' b'10992' b'454504' b'11924' b'91280' b'24000' b'173052'
b'565772' b'8216' b'785820' b'22160' b'5576' b'41524' b'95160' b'67552'
b'1512' b'153472' b'5704' b'11560' b'168' b'80536' b'54988' b'71804'
b'5616' b'64952' b'63380' b'22252' b'84160' b'2676' b'823960' b'11848'
b'64228' b'555808' b'37684' b'83740' b'34380' b'64280' b'2704' b'49664'
b'60444' b'100308' b'2516' b'74700' b'10104' b'16600' b'7224' b'108'
b'1244' b'8252' b'4196' b'957296' b'11708' b'68196' b'45080' b'82604'
b'966160' b'962964' b'444016' b'561804' b'7492' b'10424' b'12436'
b'784484' b'7108' b'44412' b'592792' b'80344' b'955756' b'92232'
b'404856' b'988072' b'3276' b'9376' b'54920' b'17756' b'3736' b'1328'
b'766544' b'4812' b'66956' b'67976' b'2568' b'2504' b'33276' b'7984'
b'4328' b'776524' b'36504' b'66332' b'51580' b'4148' b'45796' b'80656'
b'92344' b'3468' b'989184' b'5636' b'57024' b'12532' b'5508' b'100664'
b'8960' b'54976' b'89560' b'4688' b'108140' b'9408' b'5444' b'443584'
b'10896' b'3808' b'970944' b'10892' b'579468' b'6568' b'1248' b'24792'
b'36824' b'45440' b'964104' b'2296' b'16144' b'6156' b'8384' b'68460'
b'10428' b'4436' b'49552' b'3324' b'5232' b'80460' b'4668' b'11488'
b'85456' b'744292' b'51372' b'147572' b'51448' b'5056' b'538596' b'36444'
b'7880' b'583488' b'5920' b'64096' b'965600' b'59580' b'771140' b'110056'
b'52672' b'76768' b'18504' b'93044' b'140912' b'71004' b'776584' b'32756'
b'1212' b'286488' b'25132' b'79380' b'2732' b'41516' b'11160' b'272088'
b'2180' b'67364' b'42600' b'455356' b'36328' b'28352' b'968940' b'30696'
b'64664' b'829024' b'40360' b'181092' b'1572' b'8768' b'203036' b'757168'
b'2860' b'52724' b'12360' b'55172' b'71128' b'87020' b'3428' b'76920'
b'5104' b'966360' b'25116' b'582564' b'749048' b'32764' b'97980'
b'606420' b'1548' b'1304' b'737736' b'2288' b'6276' b'683160' b'75256'
b'9888' b'4956' b'51444' b'65904' b'5672' b'234984' b'68500' b'48664'
b'362508' b'36528' b'6224' b'4104' b'49452' b'3868' b'41584' b'105704'
b'77100' b'452160' b'2244']
Feature: top_3_60_day_non_fresh_product
Tensor: [b'8676' b'1284' b'65452' b'119632' b'1356' b'2624' b'968504' b'8180'
b'2572' b'5844' b'51504' b'64888' b'70312' b'10756' b'609960' b'2412'
b'1500' b'8500' b'1232' b'7152' b'65444' b'62732' b'7668' b'17756'
b'2144' b'1244' b'12556' b'2732' b'28360' b'2488' b'1424' b'244148'
b'1352' b'5464' b'12044' b'11424' b'66564' b'10208' b'1276' b'234900'
b'2132' b'2192' b'749048' b'2492' b'7108' b'48688' b'3056' b'1228' b'88'
b'14764' b'124' b'24' b'36528' b'6668' b'108104' b'2280' b'3464' b'70516'
b'1560' b'31112' b'1184' b'4816' b'41872' b'60944' b'100244' b'2860'
b'3720' b'66460' b'3880' b'48736' b'1516' b'10092' b'86780' b'2296'
b'52004' b'10104' b'2352' b'10956' b'966464' b'982392' b'490528' b'51448'
b'51784' b'404856' b'70784' b'6276' b'87036' b'64596' b'34664' b'11220'
b'3744' b'6212' b'2188' b'40' b'1452' b'65052' b'956856' b'8756' b'52652'
b'5872' b'73088' b'36584' b'57152' b'1312' b'60904' b'2456' b'234984'
b'65096' b'14808' b'6348' b'3200' b'559092' b'51292' b'14588' b'300012'
b'64636' b'57084' b'10808' b'56160' b'59580' b'19172' b'1240' b'2608'
b'75152' b'2508' b'42424' b'6156' b'80460' b'44480' b'66108' b'749928'
b'81684' b'2520' b'62252' b'2332' b'753992' b'37748' b'12136' b'6564'
b'25108' b'14628' b'3144' b'344412' b'51444' b'51372' b'1220' b'2552'
b'3092' b'7136' b'160' b'34580' b'67692' b'66476' b'399060' b'71132'
b'309828' b'2704' b'3484' b'14524' b'24816' b'17452' b'70444' b'12176'
b'135372' b'4564' b'277808' b'300016' b'1568' b'49712' b'610360' b'5080'
b'771140' b'49684' b'9040' b'2140' b'112988' b'26352' b'73276' b'258728'
b'949352' b'60856' b'2256' b'1252' b'12516' b'11944' b'16708' b'4780'
b'5616' b'7492' b'8408' b'68560' b'9628' b'1344' b'71500' b'7424'
b'964960' b'3808' b'76808' b'105632' b'67244' b'5228' b'66492' b'191112'
b'761164' b'4196' b'913772' b'36444' b'3608' b'67216' b'958324' b'8412'
b'12324' b'1456' b'8768' b'2452' b'84160' b'68304' b'5104' b'15920'
b'6352' b'5348' b'68276' b'6568' b'45528' b'28' b'7880' b'89688'
b'682264' b'24912' b'5672' b'12552' b'51752' b'20808' b'2588' b'4408'
b'181936' b'4704' b'97792' b'906288' b'3596' b'25128' b'84020' b'66568'
b'90236' b'557064' b'71864' b'331880' b'328828' b'86420' b'447028'
b'75124' b'5344' b'6132' b'51620' b'227660' b'115876' b'64088' b'596044'
b'2512' b'597420' b'36852' b'36504' b'199800' b'9504' b'8556' b'11664'
b'32772' b'33412' b'49760' b'3480' b'3816' b'442416' b'64952' b'48304'
b'65812' b'780480' b'10896' b'52492' b'33044' b'782860' b'7584' b'16536'
b'45612' b'4936' b'9956' b'766024' b'11512' b'8064' b'4524' b'10036'
b'48264' b'74704' b'51660' b'42476' b'11344' b'232832' b'2848' b'51716'
b'5704' b'6052' b'908244' b'7224' b'1368' b'24940' b'32936' b'20152'
b'989584' b'11836' b'2388' b'20' b'1740' b'16600' b'3488' b'65428'
b'22160' b'1756' b'11236' b'57004' b'211980' b'56804' b'8384' b'2208'
b'3920' b'450356' b'989708' b'91280' b'8388' b'577000' b'2440' b'357996'
b'77380' b'9544' b'64228' b'3476' b'64280' b'587804' b'557748' b'NA'
b'957176' b'12096' b'11848' b'55064' b'48412' b'1464' b'3736' b'9408'
b'967048' b'101384' b'444016' b'912344' b'65968' b'12636' b'3668'
b'251808' b'12716' b'4112' b'24988' b'682952' b'140004' b'577092' b'5236'
b'96208' b'501788' b'54464' b'6512' b'776584' b'60396' b'3344' b'785236'
b'455204' b'83872' b'1404' b'12544' b'908236' b'55256' b'65216' b'71004'
b'99868' b'7892' b'1304' b'488608' b'25116' b'108' b'47936' b'7736'
b'770056' b'65696' b'65828' b'5444' b'11432' b'6548' b'91128' b'110196'
b'36568' b'2416' b'9904' b'908208' b'56996' b'19356' b'786928' b'5944'
b'117412' b'4572' b'83132' b'597236' b'5136' b'75656' b'502228' b'54976'
b'16652' b'65892' b'67364' b'17528' b'75256' b'184192' b'66320' b'28344'
b'19740' b'8592' b'9056' b'754876' b'48608' b'7072' b'65972' b'3784'
b'583288' b'744460' b'82364' b'175656' b'12436' b'1556' b'5056' b'2516'
b'3124' b'7984' b'19192' b'52220' b'586936' b'149840' b'59640' b'7572'
b'125172' b'51556' b'496468' b'79384' b'2776' b'42912' b'24788' b'63196'
b'8260' b'30816' b'551076' b'81596' b'36848' b'81908' b'9756' b'499452'
b'7532' b'613884' b'42444' b'903488' b'59332' b'40396' b'770864' b'2268'
b'70636' b'3096' b'9376' b'8888' b'70292' b'485780' b'80776' b'11024'
b'1272' b'5240' b'538184' b'4880' b'67976' b'603544' b'64708' b'36824'
b'12272' b'86436' b'51416' b'200112' b'411544' b'14732' b'6392' b'957816'
b'4520' b'12360' b'45796' b'14560' b'5004' b'528252' b'51364' b'36396'
b'766544' b'49764' b'4948' b'51484' b'42612' b'67704' b'32800' b'12656'
b'78052' b'1736' b'5584' b'26828' b'913992' b'12676' b'6452' b'76'
b'1324' b'72060']
Feature: top_4_60_day_non_fresh_product
Tensor: [b'88' b'6212' b'1424' b'1500' b'118652' b'124' b'1352' b'174456'
b'300016' b'51448' b'41872' b'80536' b'66088' b'12552' b'6664' b'2732'
b'2508' b'2132' b'65336' b'66564' b'62080' b'14760' b'6364' b'17756'
b'19124' b'45520' b'66108' b'66460' b'3600' b'1284' b'81096' b'300012'
b'25108' b'949352' b'67704' b'1356' b'2556' b'99648' b'2412' b'12176'
b'2192' b'4408' b'63120' b'2416' b'34580' b'1452' b'2704' b'57164'
b'366352' b'173052' b'6380' b'1312' b'61448' b'5312' b'1560' b'1332'
b'12136' b'173436' b'11512' b'5080' b'78384' b'41864' b'71128' b'2144'
b'2460' b'60856' b'9676' b'90404' b'5228' b'444016' b'51504' b'84652'
b'79040' b'91384' b'36824' b'11472' b'293556' b'682264' b'119880'
b'453788' b'60944' b'49552' b'14628' b'503952' b'64616' b'11236' b'1208'
b'51412' b'7108' b'4816' b'1472' b'749048' b'65896' b'2304' b'331880'
b'5240' b'968576' b'65824' b'7152' b'3744' b'3056' b'61696' b'1596'
b'2492' b'450748' b'51304' b'482208' b'64596' b'5332' b'1276' b'9672'
b'12344' b'6844' b'530580' b'20' b'3200' b'598592' b'82836' b'2848'
b'67412' b'9456' b'12260' b'1344' b'908208' b'583488' b'2608' b'10892'
b'30888' b'66476' b'31116' b'213108' b'16600' b'1304' b'36488' b'5344'
b'113192' b'1228' b'3276' b'5736' b'10104' b'41780' b'2572' b'51736'
b'1232' b'9904' b'110032' b'36504' b'108104' b'4564' b'51672' b'56472'
b'65932' b'7500' b'3464' b'83272' b'3392' b'999344' b'70428' b'7104'
b'7224' b'10036' b'140036' b'52044' b'611000' b'16272' b'44392' b'76'
b'5104' b'174788' b'78436' b'91936' b'71440' b'40620' b'6156' b'1456'
b'987808' b'68236' b'8388' b'67976' b'28' b'1324' b'8500' b'41524'
b'60840' b'3532' b'3476' b'14732' b'49732' b'56136' b'3808' b'4160'
b'464592' b'2552' b'3144' b'104968' b'108' b'2568' b'71452' b'65016'
b'54436' b'6348' b'2180' b'160' b'42424' b'57152' b'10428' b'8216'
b'6132' b'66320' b'2456' b'196688' b'68572' b'49664' b'61064' b'7492'
b'8880' b'80068' b'45440' b'3920' b'8556' b'404832' b'5872' b'65444'
b'375516' b'12656' b'68276' b'7712' b'16800' b'6568' b'83872' b'3564'
b'3596' b'1184' b'68216' b'64228' b'48664' b'7508' b'36444' b'7560'
b'10792' b'244148' b'NA' b'761168' b'150100' b'65424' b'10092' b'64088'
b'811500' b'73244' b'948284' b'95648' b'50708' b'65492' b'780244'
b'81892' b'57496' b'949792' b'8896' b'66796' b'771296' b'3736' b'4192'
b'9956' b'454264' b'1432' b'43196' b'35228' b'1420' b'6352' b'4700'
b'5136' b'76588' b'48412' b'5236' b'4040' b'2208' b'168' b'120' b'22100'
b'4316' b'44092' b'67364' b'7668' b'9544' b'61112' b'65436' b'2512'
b'234900' b'27300' b'24' b'80460' b'357704' b'14524' b'5464' b'440168'
b'11488' b'48276' b'16668' b'60440' b'16544' b'18448' b'64664' b'109364'
b'467604' b'64136' b'746024' b'908244' b'26404' b'74704' b'21152' b'32'
b'12556' b'78308' b'66568' b'5688' b'65428' b'92164' b'597840' b'36820'
b'66448' b'43528' b'44416' b'51484' b'54976' b'11848' b'65216' b'71428'
b'124800' b'3236' b'3488' b'75124' b'63400' b'9040' b'1748' b'25116'
b'681956' b'908236' b'3480' b'146784' b'45244' b'164' b'60940' b'12636'
b'3468' b'36568' b'16696' b'51372' b'16176' b'60928' b'11664' b'52004'
b'10636' b'14532' b'65816' b'14500' b'904524' b'51716' b'2440' b'84160'
b'83740' b'50436' b'766024' b'22252' b'950768' b'64824' b'588048'
b'48704' b'175644' b'1212' b'79408' b'5404' b'736060' b'96216' b'51292'
b'184636' b'8644' b'78544' b'7884' b'36848' b'16536' b'67508' b'2352'
b'3904' b'1404' b'26804' b'41040' b'2516' b'4328' b'82996' b'3668'
b'190232' b'92668' b'489120' b'8720' b'7616' b'99288' b'502584' b'10384'
b'103364' b'77868' b'16168' b'70516' b'76144' b'152900' b'83900' b'25128'
b'8064' b'344412' b'8452' b'19740' b'48680' b'9408' b'1464' b'19172'
b'611596' b'8252' b'2236' b'908212' b'51752' b'514072' b'7080' b'60272'
b'65812' b'488608' b'75256' b'65916' b'11468' b'24916' b'21300' b'51436'
b'4380' b'8384' b'68468' b'8540' b'15920' b'51444' b'131140' b'55336'
b'6204' b'405768' b'1256' b'66452' b'31068' b'5444' b'48624' b'7716'
b'530380' b'182520' b'33108' b'903488' b'25784' b'66256' b'86140'
b'771140' b'1320' b'965648' b'10808' b'2860' b'7228' b'141012' b'7980'
b'86444' b'309828' b'1460' b'6968' b'595828' b'4936' b'24836' b'9376'
b'22188' b'41496' b'2140' b'430812' b'7876' b'99880' b'756964' b'373576'
b'4948' b'2248' b'28660' b'565660' b'3124' b'1220' b'9416' b'5508'
b'809012' b'50876' b'6392' b'2864' b'379780' b'1496' b'97408' b'12620'
b'64100' b'443368' b'64636' b'2296' b'573728' b'6240' b'1328' b'684088'
b'11344' b'117400' b'48936' b'1308' b'4520' b'279408' b'914476' b'2264'
b'66532' b'753992' b'36836' b'24932' b'326180' b'9872' b'1548' b'226108'
b'64096' b'17392' b'42912' b'2776' b'52220' b'7176' b'5008' b'957504'
b'10832' b'5476' b'45364' b'56540' b'67688' b'103080' b'79644' b'819108'
b'533496' b'3720' b'6612' b'4908' b'2532' b'12672' b'150352' b'749112']
Feature: top_5_60_day_non_fresh_product
Tensor: [b'1284' b'12092' b'1500' b'64616' b'31068' b'1424' b'777928' b'757168'
b'2440' b'51504' b'54988' b'945672' b'65444' b'736296' b'193904' b'5240'
b'9740' b'1356' b'65932' b'1220' b'47936' b'2624' b'1212' b'124' b'1452'
b'949352' b'74704' b'3596' b'68204' b'300016' b'565168' b'16456' b'60904'
b'2492' b'6568' b'1232' b'68276' b'1572' b'565660' b'80460' b'2608'
b'2264' b'544116' b'64088' b'455204' b'87016' b'83740' b'19740' b'105744'
b'41872' b'9904' b'17756' b'3428' b'1456' b'88' b'12436' b'37752'
b'65016' b'2556' b'2256' b'113192' b'1228' b'7224' b'4196' b'34384'
b'97792' b'8500' b'152556' b'65436' b'66108' b'461164' b'51292' b'1352'
b'65828' b'5596' b'7152' b'2352' b'65140' b'79160' b'771140' b'64824'
b'56584' b'8216' b'6348' b'482992' b'2388' b'5812' b'50876' b'70364'
b'2572' b'98044' b'12032' b'10036' b'125284' b'22252' b'400124' b'566300'
b'78000' b'2704' b'9344' b'12136' b'82864' b'65496' b'66452' b'3668'
b'5508' b'6156' b'1620' b'2848' b'66312' b'946780' b'1568' b'443832'
b'11848' b'92232' b'914476' b'2132' b'2520' b'64704' b'2412' b'7720'
b'70624' b'6448' b'908236' b'3488' b'1312' b'956972' b'84720' b'44796'
b'80344' b'5624' b'2512' b'2516' b'54984' b'49664' b'5576' b'16160'
b'5444' b'374376' b'293556' b'3744' b'2540' b'51348' b'117136' b'76224'
b'81248' b'2140' b'8672' b'104796' b'9544' b'1276' b'3144' b'12656'
b'76564' b'184120' b'8464' b'25692' b'10428' b'150304' b'171184' b'55380'
b'10412' b'765808' b'599032' b'52048' b'2304' b'24584' b'64748' b'908244'
b'1332' b'24788' b'193816' b'43260' b'8384' b'57152' b'595936' b'4948'
b'3476' b'14584' b'3464' b'71428' b'50552' b'9320' b'583488' b'6276'
b'2456' b'767916' b'65248' b'66376' b'117440' b'63016' b'28' b'65836'
b'60856' b'10908' b'27220' b'7816' b'78876' b'4408' b'3124' b'3920'
b'16168' b'2192' b'76764' b'44412' b'65420' b'5736' b'2504' b'3408'
b'48936' b'105900' b'1184' b'11444' b'66332' b'65336' b'5616' b'7220'
b'2568' b'3604' b'5080' b'12400' b'70516' b'51372' b'24' b'60388' b'3824'
b'17724' b'6132' b'207580' b'6072' b'581856' b'36348' b'8388' b'2688'
b'110204' b'12176' b'170588' b'2188' b'32936' b'2392' b'20828' b'784180'
b'66040' b'1528' b'19928' b'66564' b'4800' b'45612' b'5236' b'NA' b'7492'
b'103984' b'9956' b'50444' b'81596' b'566344' b'65096' b'7216' b'2148'
b'116456' b'1244' b'32' b'9676' b'185588' b'4564' b'184704' b'66320'
b'88408' b'3060' b'9504' b'1328' b'11656' b'3200' b'66460' b'105716'
b'60444' b'71484' b'51752' b'5664' b'955608' b'55476' b'1404' b'2332'
b'8556' b'54976' b'85388' b'57116' b'785236' b'4508' b'40920' b'1560'
b'160' b'10092' b'8552' b'20' b'67976' b'7736' b'244148' b'3720' b'3424'
b'82996' b'42488' b'36848' b'7572' b'3444' b'1280' b'7668' b'70728'
b'44692' b'557344' b'33460' b'2732' b'51444' b'7108' b'11560' b'77096'
b'537452' b'33108' b'64252' b'1368' b'277808' b'60940' b'489216' b'2280'
b'557064' b'64096' b'6212' b'482220' b'108940' b'9408' b'61064' b'564856'
b'3460' b'3344' b'24836' b'813516' b'45652' b'51304' b'3808' b'2672'
b'78124' b'7880' b'65896' b'2108' b'16536' b'7704' b'908208' b'300012'
b'205884' b'45368' b'56088' b'65816' b'51320' b'84156' b'1200' b'16176'
b'14524' b'82836' b'84652' b'111260' b'989708' b'76' b'1240' b'11168'
b'466720' b'21676' b'7716' b'344412' b'3880' b'788508' b'31116' b'52460'
b'70396' b'36312' b'65524' b'3056' b'737736' b'91280' b'85036' b'115332'
b'3468' b'84016' b'25128' b'83048' b'4816' b'24940' b'813504' b'558208'
b'56624' b'84' b'4104' b'10184' b'57620' b'12544' b'247004' b'72'
b'51448' b'56076' b'452164' b'1552' b'4172' b'7336' b'19512' b'406252'
b'12204' b'36568' b'45204' b'51584' b'464592' b'108' b'51784' b'71440'
b'761392' b'7632' b'2508' b'10808' b'76388' b'19172' b'5228' b'12848'
b'66568' b'3600' b'1188' b'601492' b'42484' b'20880' b'79340' b'965524'
b'80216' b'19188' b'91936' b'48600' b'113520' b'16224' b'923888'
b'505332' b'112776' b'88112' b'5332' b'36712' b'614040' b'123432'
b'16236' b'1272' b'73088' b'55048' b'93620' b'66476' b'682264' b'2696'
b'11732' b'32960' b'3516' b'65832' b'3828' b'3564' b'56344' b'26068'
b'11836' b'93044' b'16600' b'30724' b'501784' b'16004' b'2208' b'84976'
b'214972' b'65488' b'42056' b'2860' b'1464' b'60396' b'66684' b'66348'
b'94992' b'7168' b'2552' b'501788' b'33276' b'64280' b'4148' b'5388'
b'102380' b'4328' b'43880' b'43612' b'99288' b'4572' b'4316' b'64136'
b'6452' b'17448' b'82368' b'2588' b'965604' b'11208' b'9868' b'4380'
b'12676' b'48264' b'95916' b'56848' b'14732' b'286488' b'598592' b'73408'
b'51484' b'7888' b'25116' b'66680' b'51864' b'5672' b'113544' b'12260'
b'430392' b'55620' b'51180' b'2296' b'14764' b'6700' b'66088' b'811132'
b'5984' b'11156' b'218024' b'102692' b'7084' b'3700' b'592792' b'2524'
b'2112' b'9804' b'34572' b'82364' b'2728' b'41516' b'67412' b'50872'
b'1440' b'6816' b'4700' b'766544' b'724144' b'14804' b'414468' b'10104'
b'1252' b'2144' b'66648' b'273356' b'1304' b'82192' b'186664' b'35688'
b'1460' b'2176' b'41532' b'51580' b'55064' b'757252' b'48556' b'75788'
b'6524' b'12056' b'15804' b'7712' b'2260' b'3480' b'59624' b'153888'
b'3220' b'25108' b'2584' b'196' b'16072' b'36488' b'7616' b'51616'
b'4524' b'16676' b'40456' b'4160' b'51876' b'6848' b'8920']
Feature: top_1_90_day_non_fresh_product
Tensor: [b'24980' b'124' b'1352' b'1356' b'2456' b'48736' b'2188' b'64632' b'6380'
b'10104' b'245488' b'2572' b'52004' b'51504' b'7152' b'66460' b'66564'
b'65336' b'8500' b'1284' b'97792' b'2496' b'64616' b'835436' b'1312'
b'1500' b'81596' b'65428' b'40908' b'88' b'1424' b'14796' b'1184'
b'399060' b'99648' b'2704' b'17756' b'5344' b'1560' b'4196' b'2492'
b'91936' b'7488' b'45244' b'77996' b'771148' b'1228' b'2412' b'24'
b'40412' b'813520' b'5080' b'10756' b'8592' b'2140' b'60396' b'64088'
b'14628' b'2732' b'31068' b'28' b'3488' b'1452' b'4408' b'1276' b'25128'
b'755440' b'7500' b'66320' b'4816' b'14828' b'7108' b'66376' b'57152'
b'20' b'2192' b'914476' b'48688' b'7736' b'118652' b'8388' b'1232'
b'8444' b'74700' b'36328' b'8172' b'6348' b'102400' b'16412' b'76224'
b'70516' b'274296' b'19740' b'51444' b'602856' b'16676' b'73268' b'3808'
b'10792' b'595936' b'908244' b'3276' b'4812' b'49712' b'67976' b'613884'
b'326180' b'66760' b'51372' b'3668' b'557064' b'8252' b'65904' b'7224'
b'603544' b'5184' b'76404' b'14764' b'2508' b'61112' b'65424' b'9384'
b'12176' b'173052' b'2132' b'3196' b'51292' b'11848' b'750000' b'36504'
b'1332' b'538028' b'2460' b'2584' b'64280' b'4564' b'2688' b'182520'
b'2256' b'2776' b'5104' b'45568' b'7608' b'65444' b'490528' b'82464'
b'44544' b'3200' b'84652' b'956856' b'2416' b'10808' b'51448' b'5704'
b'488608' b'63996' b'9416' b'87612' b'147824' b'82996' b'65932' b'12552'
b'502228' b'2556' b'10896' b'73244' b'25108' b'2516' b'14480' b'293556'
b'2860' b'64664' b'2512' b'955216' b'3480' b'83900' b'4016' b'24912'
b'908212' b'59608' b'48276' b'5444' b'96600' b'7176' b'908208' b'153472'
b'84372' b'87016' b'4704' b'6612' b'36568' b'95460' b'68196' b'3744'
b'737736' b'823960' b'83740' b'12556' b'60856' b'16600' b'949656'
b'56088' b'25132' b'48704' b'78672' b'65836' b'12540' b'539436' b'966160'
b'957504' b'41872' b'77908' b'7492' b'10424' b'300016' b'28376' b'581988'
b'64056' b'48900' b'16696' b'9956' b'8384' b'92232' b'365544' b'89136'
b'2452' b'5228' b'140004' b'588196' b'16116' b'9884' b'1420' b'3720'
b'501784' b'64824' b'215712' b'540040' b'1240' b'2540' b'83872' b'6212'
b'45796' b'110032' b'11924' b'92344' b'11760' b'1360' b'60940' b'233732'
b'5508' b'71428' b'5332' b'331880' b'442208' b'2112' b'6664' b'6156'
b'142452' b'66148' b'514072' b'3144' b'7984' b'144768' b'24836' b'537452'
b'68468' b'16144' b'51556' b'12544' b'51320' b'16748' b'66348' b'3028'
b'66796' b'24816' b'2144' b'3344' b'51716' b'597344' b'6568' b'81296'
b'49756' b'62916' b'4700' b'1304' b'746308' b'2520' b'2588' b'78052'
b'4948' b'92164' b'966464' b'60444' b'4160' b'108' b'17596' b'67244'
b'9544' b'77936' b'18948' b'76396' b'85348' b'12056' b'65016' b'121212'
b'3476' b'7712' b'2248' b'574696' b'5404' b'181092' b'65696' b'444016'
b'16' b'761164' b'33276' b'6452' b'36512' b'2280' b'6364' b'452164'
b'65488' b'54988' b'454264' b'583488' b'6072' b'97980' b'9040' b'3736'
b'36444' b'6276' b'83596' b'64112' b'60904' b'12360' b'3516' b'5476'
b'782776' b'234984' b'75788' b'49448' b'362508' b'20984' b'4560' b'97408'
b'183240' b'65288' b'306644' b'51868' b'12672' b'55240' b'56460']
Feature: top_2_90_day_non_fresh_product
Tensor: [b'24984' b'2572' b'65436' b'1352' b'1228' b'124' b'508720' b'8180'
b'65932' b'956856' b'2144' b'6816' b'1500' b'5344' b'1312' b'64824'
b'2192' b'66564' b'70516' b'94472' b'1356' b'11488' b'88' b'57152'
b'1424' b'2488' b'2732' b'64888' b'244148' b'51504' b'12556' b'11424'
b'54436' b'4116' b'11200' b'54524' b'4408' b'10756' b'3668' b'2492'
b'66460' b'14796' b'14756' b'7108' b'8888' b'24836' b'19420' b'4240'
b'28' b'66376' b'300012' b'65812' b'1284' b'12136' b'24' b'63120' b'6668'
b'1184' b'142952' b'78384' b'40456' b'945984' b'7736' b'6664' b'444016'
b'2860' b'2584' b'67216' b'3880' b'5240' b'64596' b'66760' b'73268'
b'530580' b'1232' b'9040' b'2688' b'84720' b'503952' b'2352' b'538028'
b'8444' b'51784' b'404856' b'5228' b'6276' b'12176' b'1452' b'65824'
b'3744' b'5616' b'2132' b'64616' b'65140' b'65052' b'494860' b'80024'
b'455204' b'9904' b'73088' b'76716' b'2520' b'6612' b'51740' b'64252'
b'66476' b'10092' b'908236' b'8500' b'12456' b'82836' b'10808' b'6568'
b'1276' b'44480' b'917780' b'9408' b'2440' b'25128' b'110032' b'51292'
b'51672' b'8388' b'37748' b'11224' b'965524' b'2556' b'452164' b'7668'
b'989584' b'7224' b'64088' b'495964' b'6156' b'116452' b'9812' b'1272'
b'2704' b'3144' b'70444' b'32920' b'65096' b'10636' b'73244' b'139848'
b'5688' b'71004' b'83872' b'455536' b'14628' b'193816' b'9416' b'63016'
b'5056' b'8592' b'9956' b'14524' b'60856' b'10792' b'3460' b'67016'
b'1560' b'11160' b'4656' b'10036' b'49756' b'49664' b'4816' b'25716'
b'6348' b'682264' b'300016' b'67244' b'56' b'454264' b'36444' b'6452'
b'41872' b'67028' b'12204' b'41864' b'2412' b'66040' b'19928' b'6352'
b'5664' b'12552' b'5084' b'7492' b'36568' b'24912' b'2140' b'3200'
b'70292' b'66252' b'20' b'16600' b'54584' b'3196' b'4704' b'184704'
b'2256' b'3124' b'14480' b'68028' b'84372' b'3236' b'908208' b'3476'
b'35228' b'119632' b'4508' b'65708' b'31116' b'213108' b'309828' b'12600'
b'67364' b'5736' b'160' b'11632' b'5080' b'65444' b'90296' b'27300'
b'60192' b'48304' b'565772' b'44692' b'2540' b'31120' b'746452' b'64228'
b'60440' b'41524' b'95160' b'76364' b'216276' b'65244' b'2516' b'5704'
b'68572' b'80536' b'48264' b'54988' b'71804' b'2508' b'64952' b'2460'
b'22252' b'84160' b'5076' b'44416' b'11848' b'555808' b'37684' b'25132'
b'34380' b'17196' b'24940' b'60444' b'100308' b'6356' b'4564' b'22160'
b'74700' b'10104' b'25116' b'2208' b'450356' b'1244' b'8172' b'31112'
b'4196' b'3056' b'146784' b'957448' b'65896' b'1368' b'11708' b'68196'
b'51716' b'45080' b'82604' b'913728' b'962964' b'4160' b'55064' b'12436'
b'7608' b'175644' b'80344' b'955756' b'25108' b'3468' b'988072' b'3276'
b'771140' b'54920' b'83740' b'16536' b'25136' b'100832' b'1404' b'108'
b'6132' b'2304' b'2568' b'2504' b'17756' b'501788' b'776524' b'36504'
b'785236' b'66956' b'66332' b'4192' b'4148' b'91720' b'51372' b'98160'
b'99868' b'8216' b'91936' b'2456' b'12532' b'100664' b'8960' b'54976'
b'11432' b'60708' b'5444' b'443584' b'67688' b'3808' b'970944' b'8384'
b'579468' b'3828' b'41032' b'567196' b'5136' b'964104' b'2296' b'501784'
b'5464' b'51444' b'65836' b'65892' b'4948' b'3324' b'11560' b'66320'
b'17596' b'1516' b'64280' b'48608' b'4668' b'85456' b'955168' b'51448'
b'3736' b'7880' b'746316' b'64096' b'65664' b'965600' b'120700' b'59580'
b'51556' b'32' b'14732' b'52672' b'6212' b'76768' b'12380' b'9376'
b'32756' b'30816' b'286488' b'5324' b'3596' b'5872' b'586936' b'2180'
b'52004' b'455356' b'59332' b'50876' b'968940' b'70636' b'64664'
b'829024' b'40360' b'281424' b'7572' b'1572' b'11024' b'10424' b'12360'
b'603544' b'3060' b'71128' b'60940' b'76920' b'51320' b'966360' b'582564'
b'749048' b'51348' b'766544' b'55156' b'1380' b'2264' b'683160' b'4956'
b'65904' b'83900' b'48664' b'4172' b'67704' b'6224' b'5236' b'3868'
b'41584' b'12676' b'105704' b'452160' b'14584' b'1324' b'150352' b'98496']
Feature: top_3_90_day_non_fresh_product
Tensor: [b'6348' b'6212' b'65452' b'119632' b'1356' b'2492' b'1352' b'24044'
b'2572' b'76224' b'41872' b'9396' b'51504' b'70312' b'10756' b'5240'
b'2412' b'1500' b'65932' b'1232' b'3440' b'65444' b'557064' b'7668'
b'17756' b'2144' b'949352' b'12556' b'9140' b'66108' b'300012' b'7712'
b'60904' b'1424' b'25108' b'66564' b'10208' b'1276' b'12176' b'65140'
b'63120' b'749048' b'2256' b'1284' b'2704' b'57164' b'2488' b'3056'
b'12436' b'1228' b'590676' b'5312' b'14764' b'8228' b'88' b'4196' b'5080'
b'97792' b'31112' b'71128' b'1184' b'9676' b'124' b'60928' b'65828'
b'113404' b'9956' b'66460' b'7152' b'11472' b'8500' b'1516' b'682264'
b'119880' b'86780' b'7108' b'10428' b'16800' b'5228' b'51660' b'5344'
b'490528' b'51448' b'28' b'2304' b'78180' b'87036' b'82864' b'4104'
b'2732' b'2188' b'40' b'81644' b'1452' b'956856' b'482208' b'2192'
b'52652' b'9672' b'36584' b'76' b'766544' b'234984' b'75352' b'65424'
b'7224' b'6568' b'51752' b'51292' b'12552' b'84720' b'17916' b'6364'
b'49664' b'59580' b'2552' b'1240' b'54984' b'6156' b'42424' b'10104'
b'2520' b'80460' b'44480' b'749928' b'81684' b'1560' b'2456' b'62252'
b'92344' b'753992' b'7500' b'6452' b'913728' b'9544' b'51444' b'70428'
b'1220' b'9344' b'10036' b'496084' b'234900' b'2132' b'399060' b'64220'
b'24812' b'5104' b'174788' b'14524' b'2540' b'9040' b'4564' b'277808'
b'300016' b'21912' b'771140' b'49684' b'10288' b'60840' b'5736' b'45644'
b'49732' b'25128' b'73276' b'49760' b'2608' b'60944' b'2568' b'71452'
b'5616' b'54436' b'16104' b'3144' b'7492' b'4520' b'57152' b'6132'
b'3200' b'1344' b'83740' b'51648' b'68572' b'90864' b'7424' b'8880'
b'5444' b'3808' b'76808' b'24' b'151936' b'51372' b'8388' b'183996'
b'4948' b'41516' b'913772' b'2632' b'68276' b'2556' b'958324' b'12780'
b'83872' b'2452' b'84160' b'19740' b'4816' b'52004' b'3488' b'102272'
b'449588' b'45612' b'12324' b'60444' b'7880' b'103980' b'64824' b'5672'
b'12540' b'71500' b'1528' b'2588' b'4408' b'917948' b'4704' b'9904'
b'95648' b'32' b'65492' b'16456' b'90236' b'66476' b'328828' b'86420'
b'771296' b'3736' b'4192' b'34608' b'44984' b'64276' b'5136' b'12532'
b'5236' b'10828' b'40920' b'11224' b'12136' b'2512' b'24988' b'36568'
b'65436' b'10992' b'42488' b'91280' b'1404' b'64952' b'65812' b'3824'
b'41864' b'3744' b'22160' b'7584' b'16544' b'64664' b'537452' b'54044'
b'1512' b'11512' b'7736' b'8064' b'168' b'91936' b'78308' b'4300'
b'63380' b'2848' b'2516' b'20000' b'5704' b'6052' b'54976' b'1368'
b'5688' b'20152' b'124800' b'2352' b'3668' b'2860' b'75124' b'63400'
b'838576' b'16600' b'65428' b'105900' b'1756' b'11236' b'40476' b'119244'
b'949832' b'67276' b'2508' b'1200' b'454264' b'48736' b'8252' b'2440'
b'10636' b'11732' b'14500' b'904524' b'587804' b'NA' b'957176' b'561804'
b'10808' b'22252' b'64088' b'65524' b'588048' b'44412' b'592792' b'2676'
b'68392' b'8920' b'36444' b'404856' b'81328' b'66568' b'65968' b'1324'
b'10896' b'57620' b'12544' b'12716' b'64280' b'452164' b'20' b'2624'
b'44888' b'577092' b'66956' b'67976' b'3484' b'96208' b'4328' b'76588'
b'6512' b'776584' b'19172' b'55256' b'65216' b'77868' b'16168' b'112988'
b'4524' b'51740' b'116388' b'77908' b'488608' b'12580' b'25116' b'47936'
b'8452' b'770056' b'65696' b'89560' b'11944' b'4688' b'34384' b'9496'
b'76732' b'108140' b'9408' b'6628' b'908208' b'60940' b'19356' b'786928'
b'75256' b'60856' b'11468' b'21300' b'83132' b'597236' b'1312' b'24792'
b'45440' b'603544' b'16676' b'48852' b'16652' b'68460' b'12184' b'1552'
b'66684' b'2280' b'91696' b'5232' b'8592' b'82836' b'102380' b'65972'
b'33108' b'82364' b'147572' b'70516' b'15920' b'3124' b'66960' b'19192'
b'5920' b'965648' b'586936' b'2776' b'149840' b'59640' b'7228' b'7572'
b'64596' b'110056' b'86444' b'2180' b'24788' b'48952' b'18504' b'93044'
b'8260' b'1212' b'551076' b'81596' b'81908' b'9756' b'65672' b'914116'
b'74752' b'613884' b'74704' b'67364' b'42600' b'903488' b'32756' b'36328'
b'75792' b'2268' b'11432' b'30696' b'12620' b'2728' b'2296' b'982568'
b'404584' b'36504' b'13776' b'757168' b'52724' b'450340' b'4880' b'32920'
b'3920' b'87020' b'3428' b'54980' b'411544' b'2416' b'606420' b'1548'
b'9872' b'57588' b'8616' b'1304' b'957816' b'11560' b'9888' b'7176'
b'5004' b'528252' b'16512' b'51364' b'2140' b'36396' b'37752' b'68500'
b'51484' b'2584' b'331880' b'533496' b'12656' b'78052' b'7616' b'1224'
b'557344' b'26828' b'45620' b'913992' b'400992' b'108']
Feature: top_4_90_day_non_fresh_product
Tensor: [b'88' b'1284' b'1424' b'1500' b'31068' b'2624' b'968504' b'82084'
b'65444' b'576656' b'51504' b'11684' b'66088' b'12552' b'3596' b'63124'
b'1312' b'1356' b'8500' b'2132' b'47936' b'65812' b'28' b'62080' b'14760'
b'1244' b'17756' b'139988' b'909192' b'120828' b'16456' b'7176' b'6568'
b'300012' b'25128' b'65016' b'2492' b'2412' b'234900' b'455536' b'81596'
b'2416' b'105744' b'9904' b'66460' b'64664' b'1456' b'124' b'1352'
b'1560' b'8388' b'66564' b'173436' b'24816' b'9344' b'3124' b'70516'
b'1276' b'445108' b'31112' b'60856' b'4816' b'41872' b'90404' b'108104'
b'2352' b'3720' b'79040' b'771140' b'2280' b'48736' b'293556' b'2572'
b'6204' b'65428' b'12672' b'2296' b'2192' b'64616' b'10104' b'565672'
b'1208' b'10956' b'400124' b'749048' b'2704' b'70784' b'5240' b'34664'
b'48756' b'71428' b'5508' b'2452' b'3056' b'4408' b'2848' b'8756' b'2440'
b'64596' b'160' b'5872' b'67328' b'914476' b'2908' b'2456' b'62996'
b'264156' b'949352' b'9456' b'12260' b'4692' b'908208' b'2608' b'65932'
b'30888' b'66476' b'957296' b'57084' b'2144' b'65828' b'4196' b'36488'
b'89384' b'113192' b'75152' b'7224' b'2348' b'6348' b'79960' b'1232'
b'4572' b'2520' b'4564' b'2332' b'8104' b'782860' b'6564' b'8444'
b'83272' b'54524' b'9376' b'999344' b'51372' b'7104' b'1212' b'3144'
b'3092' b'738512' b'55380' b'611000' b'599536' b'944220' b'57152'
b'51784' b'2496' b'17452' b'20' b'193816' b'24' b'50876' b'3200' b'22308'
b'3476' b'7108' b'64088' b'65816' b'911360' b'12556' b'11848' b'3484'
b'9956' b'55620' b'3744' b'14732' b'14796' b'12176' b'36824' b'5080'
b'12664' b'78876' b'3488' b'595828' b'9780' b'11944' b'76764' b'584428'
b'6364' b'68560' b'10428' b'245488' b'6356' b'97080' b'19740' b'7492'
b'7220' b'60940' b'68512' b'2508' b'51292' b'404832' b'60388' b'5228'
b'66492' b'191112' b'7632' b'2732' b'908236' b'2568' b'331880' b'67216'
b'1228' b'7712' b'16800' b'8768' b'3564' b'76200' b'1452' b'68216'
b'24916' b'1528' b'7508' b'5348' b'7560' b'45528' b'NA' b'34740' b'89688'
b'44484' b'20808' b'65424' b'957420' b'1184' b'9676' b'70312' b'68572'
b'84020' b'57496' b'949792' b'4948' b'36388' b'8300' b'75124' b'60444'
b'5344' b'601112' b'227660' b'99992' b'2256' b'70476' b'2512' b'65836'
b'6352' b'4700' b'551012' b'36852' b'36504' b'16600' b'9504' b'65096'
b'120' b'51580' b'44092' b'7668' b'36444' b'32772' b'244148' b'61112'
b'33412' b'300016' b'48704' b'11924' b'1220' b'24000' b'2516' b'3668'
b'8216' b'10896' b'52492' b'440168' b'11488' b'785820' b'33044' b'5576'
b'51716' b'16536' b'18448' b'4524' b'16676' b'64136' b'86924' b'67552'
b'2148' b'277808' b'205884' b'88716' b'11560' b'65672' b'10036' b'66568'
b'44480' b'11344' b'92164' b'65148' b'36820' b'908212' b'55064' b'908244'
b'51484' b'65216' b'32936' b'2488' b'922404' b'3236' b'541232' b'9040'
b'1740' b'5236' b'10808' b'81512' b'1332' b'3480' b'71080' b'45244'
b'56804' b'64340' b'8556' b'592792' b'82108' b'12636' b'989708' b'51444'
b'14524' b'14480' b'577000' b'357996' b'66348' b'60928' b'11768' b'64228'
b'21676' b'6132' b'5704' b'6212' b'557748' b'50436' b'766024' b'538028'
b'48412' b'1464' b'3736' b'784484' b'967048' b'36568' b'1240' b'444016'
b'79408' b'7936' b'912344' b'111260' b'813504' b'122128' b'251808'
b'247004' b'7884' b'4112' b'36848' b'1328' b'25108' b'8252' b'78308'
b'96684' b'22848' b'4812' b'41040' b'7984' b'82996' b'87016' b'190232'
b'92668' b'3344' b'455204' b'8720' b'7616' b'71440' b'12544' b'67704'
b'71004' b'404856' b'53648' b'816152' b'579468' b'57024' b'61588' b'6156'
b'2688' b'6548' b'611596' b'110196' b'45572' b'51752' b'179880' b'64096'
b'93620' b'488608' b'5944' b'10892' b'11732' b'32960' b'3516' b'117412'
b'5688' b'40456' b'598956' b'11772' b'502228' b'959932' b'54976'
b'214972' b'5476' b'104968' b'49552' b'405768' b'4104' b'7168' b'501788'
b'5444' b'113056' b'5388' b'80460' b'214408' b'530380' b'4328' b'43684'
b'583288' b'175656' b'67244' b'12436' b'1556' b'25784' b'42424' b'48268'
b'52220' b'7584' b'544116' b'2860' b'12676' b'11044' b'6612' b'14764'
b'65436' b'82852' b'2776' b'1460' b'4364' b'63380' b'4464' b'76' b'2140'
b'7876' b'87424' b'65904' b'272088' b'561920' b'15920' b'80780' b'753992'
b'603544' b'2584' b'11156' b'83900' b'770864' b'76132' b'543076'
b'379780' b'3096' b'97408' b'51556' b'4248' b'64100' b'34572' b'64636'
b'70292' b'41516' b'5104' b'80776' b'121356' b'770056' b'3880' b'1272'
b'538184' b'766544' b'50848' b'3460' b'12656' b'86436' b'54316' b'76252'
b'200112' b'279408' b'83872' b'2264' b'66532' b'32764' b'36836' b'2152'
b'24912' b'326180' b'35688' b'2288' b'75928' b'7888' b'74812' b'75256'
b'45796' b'14560' b'5008' b'957504' b'448748' b'78384' b'2436' b'45364'
b'56540' b'67688' b'737736' b'36528' b'11468' b'1344' b'54988' b'56'
b'49452' b'586028' b'76220' b'16696' b'61428' b'2244']
Feature: top_5_90_day_non_fresh_product
Tensor: [b'2192' b'12092' b'1500' b'2572' b'118652' b'1424' b'777928' b'922968'
b'51580' b'3596' b'54988' b'13860' b'25128' b'736552' b'6664' b'12656'
b'88' b'1220' b'65336' b'2624' b'62732' b'4408' b'1452' b'949352' b'6364'
b'85648' b'2872' b'300016' b'66460' b'3600' b'1284' b'2492' b'4192'
b'1232' b'65428' b'1560' b'541712' b'80460' b'2608' b'99648' b'54980'
b'64088' b'455204' b'87016' b'66564' b'34580' b'14760' b'5672' b'75124'
b'12324' b'173052' b'3344' b'1276' b'1356' b'2552' b'16160' b'817352'
b'113192' b'97792' b'36528' b'4112' b'1352' b'45612' b'5988' b'41864'
b'73540' b'2144' b'12640' b'51292' b'11220' b'10756' b'17680' b'464592'
b'9792' b'7152' b'84652' b'21676' b'1228' b'3488' b'56584' b'2352'
b'2556' b'6348' b'6212' b'2388' b'16524' b'49552' b'19740' b'9416'
b'51504' b'98044' b'1184' b'12032' b'3200' b'982392' b'2132' b'1472'
b'2540' b'10104' b'331880' b'6548' b'124696' b'3744' b'6156' b'3828'
b'1620' b'4284' b'1596' b'450748' b'51304' b'124' b'59868' b'11848'
b'92232' b'12344' b'65696' b'3120' b'60904' b'598592' b'66348' b'2848'
b'65096' b'14808' b'12436' b'6448' b'559092' b'10892' b'450624' b'64636'
b'64952' b'16600' b'56160' b'44796' b'5344' b'108' b'3476' b'1312'
b'16456' b'2732' b'1368' b'9676' b'5444' b'10296' b'36504' b'293556'
b'9504' b'8556' b'14628' b'67688' b'10808' b'4656' b'7136' b'5652'
b'64664' b'10412' b'16272' b'44392' b'5576' b'10036' b'51448' b'7224'
b'24584' b'78436' b'91936' b'24788' b'9408' b'1324' b'4816' b'4948'
b'565168' b'4572' b'16236' b'94352' b'12136' b'2508' b'6844' b'169888'
b'205884' b'41524' b'66376' b'65524' b'51660' b'66760' b'65260' b'186656'
b'906284' b'538028' b'3144' b'6220' b'3920' b'24' b'12516' b'31112'
b'16708' b'11444' b'4196' b'41872' b'65420' b'2504' b'31068' b'8216'
b'12848' b'66320' b'196688' b'61064' b'28' b'964960' b'45440' b'3532'
b'65444' b'99956' b'5688' b'5704' b'957448' b'375516' b'43976' b'207580'
b'48408' b'908212' b'3608' b'3124' b'16268' b'45600' b'12176' b'2392'
b'2568' b'68304' b'84372' b'8384' b'45572' b'15920' b'8500' b'2704'
b'65436' b'8388' b'10792' b'6032' b'7876' b'NA' b'4520' b'761168'
b'34748' b'50444' b'81596' b'37576' b'42912' b'10092' b'6568' b'32'
b'73244' b'51444' b'948284' b'11224' b'4708' b'50708' b'604164' b'66568'
b'1328' b'11940' b'11656' b'2440' b'28864' b'105716' b'600900' b'9956'
b'454264' b'7980' b'43196' b'35224' b'76776' b'5184' b'597420' b'54976'
b'76588' b'944116' b'199800' b'79608' b'22100' b'32936' b'357996'
b'945672' b'9544' b'3424' b'234900' b'454504' b'1380' b'2412' b'53668'
b'36312' b'45568' b'1304' b'203404' b'84852' b'3464' b'81016' b'6612'
b'7108' b'11560' b'2248' b'33108' b'64252' b'20' b'5228' b'603828'
b'7492' b'74704' b'60444' b'66332' b'2280' b'67644' b'66476' b'809012'
b'64096' b'42476' b'108940' b'597840' b'2408' b'14560' b'3460' b'2676'
b'5136' b'1332' b'14500' b'3056' b'65836' b'24940' b'17772' b'989584'
b'97408' b'78124' b'7880' b'3808' b'16536' b'551012' b'1748' b'300012'
b'7572' b'908236' b'1572' b'2688' b'174788' b'211980' b'94160' b'65816'
b'7712' b'3444' b'36568' b'1496' b'16176' b'66088' b'77380' b'11168'
b'11664' b'89980' b'957296' b'344412' b'48600' b'3880' b'586936' b'57152'
b'244148' b'24356' b'2180' b'749048' b'54348' b'101384' b'452160'
b'115332' b'13824' b'10108' b'75584' b'16544' b'5404' b'3060' b'51372'
b'83048' b'99472' b'96216' b'36848' b'1488' b'9376' b'3384' b'506276'
b'55620' b'61112' b'908208' b'8768' b'25116' b'4172' b'682952' b'766544'
b'47852' b'60944' b'49664' b'488608' b'5236' b'51584' b'4328' b'33276'
b'91720' b'56804' b'71440' b'2348' b'60396' b'7176' b'681956' b'9040'
b'502584' b'65664' b'169984' b'9768' b'1456' b'989184' b'51752' b'113520'
b'68276' b'68252' b'36712' b'19172' b'8252' b'14580' b'40360' b'82836'
b'140256' b'66108' b'682264' b'4564' b'65916' b'12676' b'24916' b'3276'
b'4380' b'2860' b'11472' b'1248' b'338572' b'36444' b'160' b'592792'
b'48968' b'40456' b'2208' b'84976' b'1212' b'131140' b'273356' b'6204'
b'4704' b'48556' b'184192' b'60452' b'55048' b'65812' b'10428' b'582000'
b'1464' b'9056' b'754876' b'48624' b'7072' b'182520' b'207192' b'744292'
b'903488' b'82996' b'3284' b'64136' b'66256' b'42444' b'86140' b'452164'
b'8592' b'14764' b'48264' b'14584' b'6132' b'496468' b'968840' b'231860'
b'6968' b'84160' b'595828' b'73248' b'9344' b'51484' b'776584' b'5388'
b'60856' b'44412' b'65496' b'78408' b'113544' b'99880' b'2488' b'499452'
b'430392' b'51180' b'28660' b'118800' b'5080' b'56592' b'181912' b'168'
b'218024' b'3700' b'2524' b'3212' b'82364' b'100032' b'12056' b'485780'
b'94792' b'3736' b'65620' b'48852' b'64100' b'103952' b'4700' b'2140'
b'724144' b'67976' b'65140' b'14524' b'64824' b'7532' b'404856' b'16652'
b'82192' b'82248' b'2420' b'24932' b'65932' b'5096' b'20904' b'6392'
b'1548' b'537168' b'108812' b'17392' b'2776' b'8448' b'24836' b'1256'
b'966160' b'16696' b'59624' b'16624' b'49764' b'2188' b'5084' b'32800'
b'16072' b'36488' b'80424' b'3824' b'4524' b'76232' b'544076']
Feature: Total_spend
Tensor: [6.3630461e+04 1.7847666e+04 8.0024941e+03 6.4460698e+03 1.7453709e+04
4.4308350e+03 8.5073848e+03 5.4749465e+04 6.0934678e+03 2.6506469e+05
5.5260361e+03 5.0634831e+05 2.9048040e+03 4.8849832e+04 3.9899341e+03
2.4073496e+04 4.1265925e+05 6.9135029e+03 7.3950122e+03 2.0685727e+04
1.9158292e+05 9.9065342e+03 6.1226729e+03 4.2850259e+03 1.2993336e+05
3.5136179e+03 4.1555161e+03 7.0525367e+04 6.2798328e+04 2.1122303e+05
6.4703608e+03 9.9786416e+03 1.0580913e+05 3.6447086e+04 1.0918341e+04
4.9065840e+03 1.0415799e+04 2.9817361e+03 3.3996421e+03 1.5835410e+03
4.4613091e+03 7.0310161e+03 1.1209932e+04 3.9660102e+04 1.5659806e+05
2.1961225e+04 1.6722324e+04 1.0948554e+04 4.1280122e+03 7.7083921e+03
4.8530078e+04 8.0993882e+03 3.0368879e+03 4.1956021e+03 1.2900231e+04
3.1426523e+04 4.8013945e+04 3.8410740e+03 3.4923149e+03 1.0119710e+05
1.8235871e+04 1.4785452e+04 5.7138301e+03 4.5418408e+03 1.8478260e+03
1.7515484e+04 2.8042200e+03 5.9766748e+03 2.3800969e+04 4.7289150e+03
2.8409148e+04 2.1378240e+03 7.3997102e+04 4.9700512e+04 1.4057811e+04
1.5703983e+04 2.6644409e+03 8.1605342e+03 1.3649616e+04 2.9378654e+04
2.2111741e+03 1.6654590e+03 2.6117496e+04 2.2319370e+03 4.4717219e+04
1.3335660e+04 7.2050488e+03 2.1243168e+04 1.6617870e+03 2.4314939e+03
1.4315310e+03 1.3983948e+04 1.2577806e+04 1.0160154e+04 2.5487371e+03
7.1774731e+03 1.0491669e+04 1.6287489e+04 1.1454849e+04 1.3574151e+04
6.9803461e+04 7.7896260e+03 1.7157061e+03 2.2284541e+03 3.2972849e+03
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7.2222842e+03 4.9901938e+03 2.9435491e+03 1.0286100e+04]
Feature: Total_trx
Tensor: [660. 572. 571. 568. 554. 523. 516. 498. 479. 459. 456. 426. 424. 416.
408. 404. 401. 399. 397. 394. 389. 372. 364. 355. 354. 347. 343. 338.
334. 326. 321. 319. 317. 315. 313. 312. 304. 301. 298. 295. 291. 290.
288. 287. 286. 284. 280. 277. 276. 275. 273. 268. 267. 262. 260. 259.
258. 255. 254. 252. 251. 249. 248. 245. 244. 243. 242. 241. 239. 238.
237. 235. 233. 232. 229. 228. 225. 224. 223. 222. 221. 220. 219. 218.
217. 216. 215. 214. 213. 211. 210. 209. 207. 206. 205. 203. 202. 201.
200. 199. 198. 197. 194. 193. 192. 191. 189. 188. 187. 186. 185. 184.
183. 182. 181. 180. 179. 178. 177. 176. 173. 172. 171. 170. 169. 168.
167. 166. 165. 164. 163. 162. 161. 160. 159. 158. 157. 156. 155. 154.
153. 152. 151. 150. 149. 148. 147. 146. 145. 144. 143. 142. 141. 140.
139. 138. 137. 136. 135. 134. 133. 132. 131. 130. 129. 128. 127. 126.
125. 124. 123. 122. 121. 120. 119. 118. 117. 116. 115. 114. 113. 112.
111. 110. 109. 108. 107. 106. 105. 104. 103. 102. 101. 100. 99. 98.
97. 96. 95. 94. 93. 92. 91. 90. 89. 88. 87. 86. 85. 84.
83. 82. 81. 80. 79. 78. 77. 76. 75.]
Feature: Total_item
Tensor: [3381. 1880. 969. 889. 2372. 772. 1207. 1875. 993. 1438. 691. 1328.
1206. 2521. 575. 1520. 6237. 561. 964. 1603. 3256. 869. 628. 1165.
4118. 801. 660. 2023. 1869. 890. 625. 970. 937. 1787. 1048. 770.
481. 428. 768. 472. 719. 1445. 4145. 4059. 965. 920. 900. 534.
2263. 739. 589. 478. 800. 1684. 2357. 484. 1469. 1958. 2194. 644.
767. 535. 435. 1144. 645. 581. 885. 646. 1382. 1212. 2026. 1478.
1300. 454. 837. 1377. 1374. 636. 505. 968. 370. 2129. 1226. 871.
1013. 677. 391. 325. 672. 888. 736. 670. 1129. 1474. 1210. 1161.
2436. 577. 464. 418. 296. 1133. 726. 400. 794. 619. 887. 680.
515. 287. 688. 1864. 398. 1211. 854. 389. 1337. 394. 913. 1459.
1702. 1448. 423. 393. 1301. 3271. 384. 488. 1171. 567. 962. 1086.
910. 1293. 1921. 437. 366. 1118. 1227. 572. 533. 632. 608. 491.
251. 754. 1115. 986. 676. 724. 1295. 1014. 675. 412. 1036. 386.
743. 576. 3939. 429. 522. 1660. 1565. 981. 1512. 734. 985. 549.
314. 495. 420. 1532. 379. 893. 338. 1015. 301. 2869. 651. 1616.
1195. 274. 365. 350. 823. 580. 1176. 511. 821. 527. 415. 413.
822. 1317. 732. 585. 874. 661. 878. 387. 816. 297. 1529. 458.
1192. 1471. 381. 348. 1619. 1163. 1611. 583. 1463. 1666. 952. 385.
356. 1231. 1649. 461. 1066. 538. 2852. 1077. 1535. 291. 207. 547.
1553. 796. 777. 835. 306. 310. 352. 448. 532. 759. 403. 344.
703. 2050. 1172. 329. 1297. 2337. 933. 1274. 915. 183. 337. 654.
1434. 307. 209. 267. 545. 480. 311. 354. 596. 468. 1157. 558.
493. 2113. 1050. 498. 1028. 217. 1053. 313. 1012. 193. 245. 1400.
233. 216. 390. 210. 1353. 1997. 304. 388. 652. 326. 617. 276.
662. 975. 327. 826. 238. 320. 1201. 811. 280. 1146. 1189. 212.
512. 786. 184. 1061. 998. 524. 303. 191. 258. 1723. 643. 248.
187. 525. 1439. 224. 839. 709. 1101. 286. 611. 502. 185. 664.
396. 462. 336. 790. 180. 789. 784. 275. 263. 921. 761. 926.
242. 474. 566. 696. 288. 203. 824. 299. 733. 1296. 243. 255.
932. 1398. 1017. 542. 341. 240. 223. 157. 529. 667. 704. 776.
261. 649. 699. 457. 346. 232. 204. 663. 514. 820. 792. 257.
312. 673. 298. 1137. 550. 1235. 471. 427. 689. 324. 383. 614.
285. 206. 769. 504. 674. 506. 188. 987. 368. 1515. 1175. 902.
1143. 208. 283. 1786. 1004. 1659. 977. 697. 422. 752. 1002. 1220.
317. 978. 439. 200. 250. 397. 202. 1246. 360. 723. 160. 687.
382. 633. 262. 612. 465. 222. 657. 1126. 246. 281. 169. 706.
765. 1179. 641. 563. 220. 164. 345. 1949. 631. 1566. 603. 241.
1470. 742. 168. 574. 227. 861. 523. 154. 1040. 361. 309. 152.
330. 319. 229. 530. 573. 679. 247. 499. 943. 896. 355. 606.
374. 1316. 225. 155. 268. 997. 271. 186. 289. 541. 1001. 903.
1039. 833. 278. 256. 115. 239. 470. 178. 117. 459. 803. 698.
1202. 1102. 162. 980. 433. 357. 1229. 335. 648. 879. 449. 996.
235. 249. 731. 228. 1668. 165. 1365. 147. 333. 166. 1441. 489.
901. 618. 745. 293. 182. 475. 219. 294. 124. 582. 593. 148.
1815. 1062. 367. 856. 376. 705. 340. 305. 895. 146. 211. 339.
961. 946. 264. 145. 1032. 778. 815. 774. 153. 1186. 989. 455.
483. 509. 1396. 358. 137. 215. 510. 1867. 1214. 194. 466. 409.
445. 813. 1437. 231. 161. 362. 451. 218. 501. 1443. 277. 172.
979. 728. 432. 519. 269. 1204. 668. 1109. 176. 199. 322. 702.
830. 252. 621. 205. 1432. 831. 834. 375. 159. 1303. 773. 144.
1621. 300. 988. 876. 1279. 482. 414. 197. 170. 369. 452. 290.
1242. 171. 174. 995. 112. 517. 1221. 421. 782. 1150. 843. 460.
624. 497. 479. 1131. 1335. 410. 328. 221. 456. 1181. 949. 1268.
84. 343. 477. 444. 121. 840. 129. 332. 791. 436. 132. 167.
441. 1197. 318. 425. 103. 347. 99. 130. 2231. 177. 111. 513.
690. 142. 588. 715. 579. 123. 122. 805. 113. 151. 295. 116.
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Feature: Total_unique_items
Tensor: [ 687. 323. 218. 166. 644. 227. 508. 373. 172. 818. 182. 776.
163. 252. 138. 355. 840. 207. 176. 402. 810. 161. 224. 367.
551. 204. 319. 381. 420. 585. 148. 372. 279. 421. 275. 294.
206. 118. 191. 112. 277. 304. 414. 537. 622. 347. 336. 193.
186. 461. 195. 179. 208. 267. 519. 451. 120. 246. 540. 478.
316. 192. 70. 500. 165. 175. 296. 216. 525. 117. 566. 484.
506. 299. 350. 428. 343. 76. 701. 358. 412. 162. 145. 77.
190. 245. 139. 147. 146. 426. 693. 234. 328. 606. 144. 88.
397. 417. 94. 78. 401. 311. 203. 150. 433. 53. 352. 563.
496. 390. 121. 364. 860. 342. 128. 362. 1023. 93. 220. 610.
313. 383. 379. 408. 721. 173. 68. 423. 511. 196. 302. 243.
254. 300. 436. 283. 580. 415. 86. 305. 233. 238. 122. 998.
141. 125. 526. 564. 425. 431. 331. 132. 601. 387. 135. 466.
136. 649. 291. 635. 156. 384. 137. 124. 293. 171. 483. 134.
276. 340. 169. 232. 326. 345. 266. 363. 178. 376. 57. 579.
403. 151. 608. 90. 140. 627. 554. 237. 734. 361. 507. 757.
108. 91. 873. 354. 595. 157. 55. 258. 573. 223. 255. 185.
142. 230. 183. 80. 399. 546. 771. 389. 527. 468. 231. 272.
509. 72. 101. 143. 214. 215. 129. 458. 646. 187. 280. 398.
73. 536. 159. 439. 181. 95. 149. 329. 104. 490. 131. 152.
382. 177. 386. 89. 60. 346. 49. 61. 394. 493. 155. 103.
679. 239. 87. 242. 502. 75. 380. 317. 325. 205. 263. 244.
282. 339. 180. 67. 213. 259. 188. 174. 241. 113. 116. 249.
260. 515. 46. 123. 473. 393. 119. 133. 98. 84. 39. 256.
341. 322. 335. 160. 153. 114. 269. 111. 310. 450. 210. 109.
235. 290. 96. 392. 168. 236. 226. 219. 495. 158. 324. 378.
285. 79. 248. 593. 85. 630. 274. 308. 407. 440. 154. 334.
97. 598. 229. 42. 321. 303. 271. 247. 63. 200. 567. 501.
437. 253. 438. 422. 314. 883. 514. 265. 81. 388. 198. 50.
643. 74. 281. 457. 99. 66. 16. 197. 54. 353. 225. 395.
17. 199. 32. 301. 513. 418. 444. 307. 662. 452. 424. 170.
529. 106. 59. 617. 429. 574. 333. 92. 105. 47. 35. 64.
672. 467. 222. 33. 130. 287. 58. 228. 31. 273. 65. 453.
479. 441. 427. 202. 465. 36. 209. 456. 743. 83. 535. 520.
40. 360. 126. 351. 619. 375. 261. 107. 270. 521. 348. 406.
447. 652. 250. 560. 356. 708. 24. 289. 833. 315. 25. 201.
682. 240. 184. 82. 41. 518. 44. 221. 257. 38. 212. 102.
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Feature: Total_unique_subclasses
Tensor: [236. 116. 101. 78. 200. 198. 139. 90. 240. 256. 84. 130. 155. 249.
111. 85. 187. 274. 83. 97. 142. 219. 148. 160. 161. 210. 66. 166.
182. 109. 133. 123. 62. 57. 132. 117. 201. 213. 156. 168. 141. 88.
75. 181. 93. 77. 99. 134. 207. 192. 76. 98. 211. 177. 143. 95.
33. 197. 122. 110. 216. 53. 194. 174. 176. 103. 175. 67. 171. 32.
221. 164. 68. 51. 74. 217. 69. 151. 220. 128. 70. 82. 39. 169.
42. 36. 159. 135. 102. 183. 34. 157. 184. 172. 60. 290. 92. 64.
45. 296. 49. 237. 50. 170. 235. 96. 195. 30. 215. 153. 58. 154.
81. 113. 59. 289. 63. 158. 165. 185. 149. 72. 40. 61. 189. 228.
144. 108. 86. 87. 137. 191. 150. 89. 119. 145. 131. 138. 146. 80.
208. 48. 27. 73. 152. 223. 136. 243. 255. 120. 56. 79. 38. 212.
105. 94. 230. 127. 35. 125. 37. 47. 115. 71. 251. 167. 199. 41.
186. 65. 129. 121. 28. 241. 52. 106. 104. 100. 43. 44. 126. 233.
23. 55. 15. 162. 118. 193. 218. 54. 24. 26. 206. 178. 276. 231.
190. 163. 112. 31. 12. 173. 9. 91. 188. 140. 196. 29. 114. 13.
14. 205. 107. 180. 179. 17. 281. 202. 19. 124. 46. 224. 203. 11.
284. 16. 238. 25. 226. 209. 22.]
Feature: Total_unique_subdepartments
Tensor: [68. 42. 41. 34. 66. 55. 69. 48. 43. 84. 33. 36. 56. 39. 60. 75. 47. 77.
38. 40. 54. 74. 44. 50. 62. 70. 30. 63. 51. 61. 53. 35. 24. 49. 67. 71.
52. 58. 45. 19. 59. 46. 31. 57. 65. 17. 26. 29. 21. 22. 28. 20. 27. 64.
85. 92. 76. 79. 32. 37. 13. 16. 25. 18. 81. 23. 72. 10. 9. 11. 14. 15.
78. 7. 73. 6. 87. 5. 8.]
Feature: Min_spend
Tensor: [0. 0.54 0.36 0.054 0.189 0.414 0.351 0.45 0.117 0.558 0.288 0.846
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0.27 0.18 0.009 0.423 0.342 0.144 0.126 0.315 0.252 0.081 0.306 0.918
0.207 0.828 0.738 0.225 0.477 0.261 0.792 0.099 0.279 1.044 0.693 0.432
0.72 0.513 0.072 0.369 0.216 1.026 1.755 1.35 0.396 0.378 0.036 0.171
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1.575 0.81 1.125 0.504 0.657 1.8 0.324 0.621 0.063 1.089 0.927 1.305
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Feature: Max_spend
Tensor: [1.6155000e+03 8.0352002e+02 2.7288000e+02 9.8550003e+01 3.4425000e+02
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Feature: days_since_last_purchase
Tensor: [ 0. 2. 1. 32. 37. 82. 36. 4. 12. 40. 3. 9. 5. 8.
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Feature: days_since_first_purchase
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Feature: Items_per_trx
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Feature: Unique_items_per_trx
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b'755258' b'409042' b'182' b'420796' b'7590056' b'100924' b'13426'
b'1966316' b'5423266' b'7752936' b'42354' b'443200' b'6114926' b'6414'
b'39414' b'8793936' b'40418' b'690920' b'2028' b'13112' b'40090' b'21828'
b'6610964' b'380988' b'384' b'391784' b'189070' b'9175152' b'45154'
b'1194310' b'34908' b'78768' b'5574' b'31288' b'6824350' b'39834'
b'44612' b'46598' b'2184766' b'578904' b'34720' b'387216' b'44680'
b'39806' b'13436' b'6' b'1011566' b'1055992' b'335404' b'42488' b'8384'
b'13552' b'3304044' b'12874' b'686832' b'314492' b'2560' b'274' b'40104'
b'231508' b'49992' b'9572610' b'116376' b'322' b'2059202' b'42232'
b'5449896' b'2283888' b'851600' b'47008' b'341932' b'8460192' b'1762012'
b'8919972' b'308898' b'9253342' b'5058274' b'3069652' b'39396' b'41190'
b'1701892' b'151338' b'158824' b'12962' b'2775016' b'445134' b'2258354'
b'2508064' b'7029696' b'268680' b'9346750' b'4220844' b'8716' b'2144600'
b'1202290' b'1933310' b'2829106' b'8934566' b'2063998' b'13378' b'45066'
b'48980' b'593940' b'2652' b'40498' b'322136' b'44944' b'8028468'
b'417552' b'7612456' b'7492566' b'3875072' b'3868872' b'2474510'
b'495566' b'56202' b'41096' b'4511010' b'1087754' b'2217176' b'613368'
b'42664' b'52100' b'5334926' b'3769458' b'80076' b'12894' b'9151844'
b'301482' b'2739188' b'5142264' b'2428698' b'197590' b'480814' b'328'
b'9490596' b'565362' b'48844' b'286' b'40846' b'342108' b'37638'
b'335910' b'12918' b'105584' b'5408764' b'8509440' b'384156' b'42168'
b'6743738' b'40658' b'5188844' b'722506' b'5536452' b'42262' b'45100'
b'12914' b'180376' b'4215708' b'346562' b'4213864' b'150' b'611150'
b'341564' b'73336' b'636190' b'228892' b'4050686' b'418046' b'4217848'
b'9087796' b'124106' b'42326' b'43190' b'7602868' b'3222166' b'4559600'
b'362374' b'8489566' b'6886520' b'3014622' b'7573488' b'5126982'
b'8972988' b'13044' b'2602716' b'339454' b'1431556' b'10930' b'7938660'
b'9645742' b'8317256' b'5325926' b'132' b'1008924' b'12906' b'37576'
b'7804492' b'43856' b'5733092' b'5437546' b'42452' b'8527452' b'4372262'
b'16086' b'54' b'42784' b'2906766' b'39954' b'12830' b'12910' b'1332120'
b'5663142' b'1454538' b'8786792' b'1148858' b'874058' b'40430' b'42844'
b'2803540' b'7274928' b'5006352' b'172330' b'964808' b'313734' b'53790'
b'8295482' b'1350484' b'9120462' b'308' b'8532322' b'896422' b'10210472'
b'8069214' b'40696' b'130144' b'105228' b'8423364' b'2727130' b'613082'
b'4285350' b'151528' b'3489610' b'8717516' b'3977188' b'97138' b'7719530'
b'270384' b'9480420' b'47672' b'1001862' b'43596' b'6369268' b'23892'
b'299666' b'2711250' b'8716280' b'4095390' b'3690' b'266' b'52164'
b'3404398' b'40326' b'2950662' b'576150' b'9173188' b'8180554' b'12952'
b'580012' b'508024' b'43666' b'7966810' b'948804' b'663212' b'8499334'
b'8061706' b'49286' b'3808' b'7881562' b'57268' b'46400' b'332402'
b'8058340' b'1322894' b'7763546' b'6402514' b'722168' b'332334'
b'6214036' b'24340' b'35094' b'5416586' b'2565688' b'166608' b'48176'
b'105370' b'41844' b'46128' b'46' b'1284120' b'277600' b'176' b'5254528'
b'441572' b'8771680' b'12924' b'7612414' b'42914' b'50822' b'43128'
b'197922' b'42370' b'874580' b'269736' b'326' b'49668' b'1250582'
b'6297860' b'279954' b'389710' b'1416766' b'40948' b'40758' b'50588'
b'1813540' b'418124' b'1192120' b'28142' b'210552' b'8273936' b'810'
b'39948' b'56366' b'7699444' b'45140' b'44094' b'2429002' b'4439448'
b'4134756' b'7949542' b'797244' b'3400600' b'243662' b'195944' b'207132'
b'1261946' b'61910' b'12880' b'1693480' b'232' b'52132' b'548628'
b'45480' b'4200516' b'1919968' b'265176' b'850780' b'44042' b'234316'
b'190326' b'13524' b'13134' b'765630' b'2551496' b'13628' b'731294'
b'678836' b'155128' b'2977876' b'139190' b'33360' b'44480' b'2351218'
b'9804124' b'1553824' b'375534' b'161530' b'309098' b'39836' b'418952'
b'635116' b'136680' b'795854' b'2556888' b'296010' b'44456' b'1655624'
b'466096' b'273262' b'753274' b'450522' b'336380' b'147980' b'904812'
b'273136' b'3765514' b'100246' b'12920' b'2144262' b'38812' b'40072'
b'1013382' b'64' b'9077888' b'9799128' b'830220' b'9131984' b'167654'
b'2274594' b'4690242' b'24' b'13068' b'2466770' b'7556600' b'6824338'
b'484984' b'12942' b'8158600' b'1024040' b'8002190' b'2921654' b'44388'
b'153690' b'37552' b'48140' b'2173166' b'8354156' b'181422' b'1069306'
b'187764' b'1170402' b'49442' b'657152' b'1768200' b'273122' b'5536498'
b'44464' b'268' b'162248' b'2211854' b'510294' b'1557280' b'4389822'
b'3106488' b'10379954' b'7334412' b'680442' b'5754078' b'6551786'
b'645874' b'9072062' b'602' b'3064814' b'40064' b'40922' b'48792'
b'353008' b'3943038' b'440860' b'41100' b'725178' b'3706522' b'49310'
b'3223828' b'2348204' b'1834646' b'9404992' b'95252' b'40180' b'2978858'
b'1639972' b'143928' b'1200390' b'376562' b'41092' b'86834' b'7659560'
b'1866' b'1546508' b'9176102' b'3721646' b'264586' b'48064' b'706158'
b'44154' b'1245808' b'151508' b'43456' b'43310' b'5382034' b'5689306'
b'762102' b'3956196' b'42466' b'195266' b'590686' b'530258' b'43496'
b'393802' b'45916' b'45384' b'2376' b'6566018' b'657838' b'147078'
b'185670' b'1993624' b'44434' b'40478' b'28340' b'9088890' b'310838'
b'9480124' b'53834' b'477574' b'45612' b'994596' b'431244' b'7086294'
b'269346' b'543774' b'1686384' b'5566196' b'3605846' b'43264' b'276'
b'8777612' b'359912' b'9485732' b'6769736' b'44744' b'115024' b'40082'
b'5721248' b'755690' b'624326' b'233502' b'44288' b'180' b'164570'
b'130534' b'1168952' b'294' b'870212' b'1214010' b'40714' b'119648'
b'467442' b'42650' b'46158' b'44840' b'39944' b'265744' b'3684446'
b'8645336' b'48252' b'272' b'9949618' b'45432' b'46476' b'37580' b'162'
b'7715552' b'44130' b'2416768' b'325264' b'188720' b'46988' b'131396'
b'231106' b'2326048' b'152022' b'45736' b'9901020' b'400144' b'732662'
b'751438' b'332102' b'366048' b'844102' b'121590' b'1165844' b'42344'
b'40840' b'9635830' b'22' b'7930874' b'125426' b'254' b'42922' b'462954'
b'1091510' b'74930' b'29434' b'40830' b'5063234' b'53192' b'12970'
b'851232' b'640460' b'196' b'265720' b'39190' b'1148866' b'9224874'
b'1225456' b'146580' b'9326534' b'999490' b'115960' b'823450' b'5091926'
b'46914' b'9319188' b'142368' b'7006352' b'7434996' b'1149362' b'1882452'
b'460948' b'6542998' b'6194036' b'596054' b'1422648' b'387560' b'304862'
b'28346' b'33670' b'374700' b'3570442' b'1745166' b'5033204' b'42330'
b'2166702' b'167458' b'13400' b'731450' b'2913876' b'202' b'13070'
b'44274' b'9489092' b'6283362' b'109570' b'42172' b'9493760' b'1667226'
b'53402' b'43482' b'744608' b'5862740' b'156770' b'3864516' b'159106'
b'192' b'38314' b'34786' b'74894' b'3603276' b'4886474' b'588436'
b'57838' b'773094' b'967110' b'312356' b'9676594' b'2849856' b'9172576'
b'6896848' b'41276' b'353386' b'7141192' b'495436' b'9391646' b'753796'
b'9986782' b'126006' b'4331106' b'6069720' b'3583172' b'46776' b'42736'
b'224640' b'922256' b'42722' b'8538678' b'12966' b'453186' b'2790012'
b'40938' b'3044676' b'2008128' b'5634786' b'236918' b'3626084' b'8359702'
b'2809316' b'3989940' b'1079580' b'10233910' b'39470' b'43792' b'7808958'
b'2964666' b'245982' b'4296148' b'1395516' b'523170' b'90' b'40968']
Feature: top_1_7_day_fresh_product
Tensor: [b'3860' b'1264' b'44' b'1260' b'NA' b'156' b'1448' b'176' b'13416'
b'8764' b'811012' b'100' b'51224' b'438616' b'1164' b'48' b'3500'
b'41608' b'1532' b'2124' b'57776' b'7732' b'1192' b'6716' b'811020'
b'79080' b'74448' b'4224' b'1180' b'555620' b'41428' b'148984' b'6732'
b'5996' b'4452' b'2764' b'6936' b'66816' b'188' b'72512' b'67576'
b'110564' b'3160' b'5752' b'52' b'420336' b'11792' b'2116' b'3048'
b'1376' b'8328' b'2312' b'949864' b'2500' b'2564' b'3176' b'10884'
b'87484' b'732924' b'49792' b'2120' b'4744' b'1340' b'2320' b'49776'
b'90068' b'49384' b'64836' b'66104' b'2692' b'1504' b'3080' b'555576'
b'264088' b'9272' b'41432' b'1372' b'811076' b'2720' b'77312' b'3188'
b'556188' b'41768' b'64128' b'245408' b'4416' b'7324' b'6000' b'6724'
b'121676' b'3376' b'1444' b'61244' b'68100' b'71800' b'9264' b'3356'
b'2316' b'76108' b'49780' b'88576' b'1172' b'123724' b'93652' b'71364'
b'32824' b'65464' b'3184' b'5552' b'67956' b'7128' b'81188' b'52112'
b'52140' b'2160' b'3504' b'57700' b'8248' b'458128' b'67940' b'151976'
b'49600' b'188700' b'81764' b'88740' b'429736' b'48012' b'4348' b'9852'
b'5712' b'5092' b'52084' b'83576' b'65568' b'41452' b'8072' b'73000'
b'914204' b'6124' b'2596']
Feature: top_2_7_day_fresh_product
Tensor: [b'44' b'8520' b'176' b'2120' b'NA' b'188' b'3356' b'1164' b'1260'
b'811428' b'2764' b'13416' b'1444' b'2692' b'1264' b'1376' b'41452'
b'133116' b'96956' b'2116' b'156' b'327444' b'4744' b'48012' b'14468'
b'52' b'1192' b'2564' b'66308' b'1372' b'57704' b'811772' b'3500'
b'462508' b'2124' b'8764' b'41432' b'52108' b'555748' b'148984' b'1176'
b'2312' b'9856' b'7324' b'49808' b'4224' b'4416' b'9264' b'100' b'3160'
b'110564' b'88740' b'1172' b'452212' b'4912' b'6640' b'3080' b'59128'
b'78348' b'9852' b'259324' b'6128' b'5692' b'93652' b'49784' b'70176'
b'48' b'5752' b'5092' b'5452' b'732952' b'811076' b'3316' b'41120'
b'49792' b'64128' b'3184' b'3732' b'76108' b'49616' b'10920' b'1504'
b'811012' b'1532' b'49788' b'6724' b'1180' b'3504' b'41428' b'2316'
b'74796' b'49384' b'41756' b'71800' b'452304' b'811120' b'215600' b'9568'
b'3176' b'235472' b'144528' b'67052' b'65964' b'5744' b'458128' b'5996'
b'811192' b'16296' b'121676' b'10000' b'4348' b'66924' b'65564' b'62604'
b'90024' b'2720' b'64132' b'4828' b'12872' b'77312' b'6732' b'3020'
b'1448' b'61244' b'3048' b'6936' b'64836' b'811024' b'3376' b'7732'
b'7872' b'2320' b'188620' b'74448' b'67116' b'41380' b'66816' b'65144'
b'67956' b'90068' b'54124' b'78388' b'4840' b'54148' b'7128' b'446792'
b'717000' b'944936' b'49780' b'16336' b'49800' b'37132' b'452220' b'2560'
b'60580' b'452240' b'839892' b'452244' b'41540' b'151976' b'41352'
b'41324' b'112436' b'67576' b'70448' b'214460' b'11792' b'41216' b'2204'
b'54160']
Feature: top_3_7_day_fresh_product
Tensor: [b'156' b'176' b'1260' b'78400' b'188' b'NA' b'1164' b'4452' b'74448'
b'1264' b'2116' b'811020' b'327444' b'41452' b'2764' b'33652' b'4744'
b'6936' b'1372' b'106660' b'62604' b'2316' b'3356' b'3160' b'148984'
b'66572' b'93652' b'3048' b'2500' b'67116' b'100' b'77312' b'2312'
b'2120' b'70412' b'1444' b'44' b'1532' b'49808' b'1176' b'1172' b'52'
b'48' b'555784' b'1192' b'11152' b'49816' b'7324' b'77808' b'11596'
b'1448' b'5048' b'1376' b'2692' b'2124' b'41204' b'74796' b'14468'
b'67956' b'10652' b'59128' b'613360' b'41768' b'3860' b'41464' b'4348'
b'75904' b'41776' b'3188' b'8248' b'49464' b'9856' b'49800' b'5752'
b'66308' b'70940' b'54124' b'811964' b'86792' b'4828' b'71800' b'1504'
b'555576' b'7872' b'4416' b'3080' b'90068' b'76108' b'49456' b'64132'
b'10920' b'8764' b'37120' b'65568' b'452324' b'5744' b'3176' b'86852'
b'48012' b'6640' b'41380' b'64128' b'110564' b'54160' b'2720' b'64572'
b'9568' b'2320' b'3184' b'41604' b'96956' b'3364' b'41428' b'452220'
b'67268' b'3500' b'452352' b'9272' b'2564' b'67576' b'120284' b'70448'
b'2160' b'6124' b'8168' b'77516' b'6724' b'41312' b'605148' b'188700'
b'3504' b'99268' b'452244' b'6128' b'1180' b'5692' b'52188' b'959780'
b'90024' b'185620' b'71284' b'555588' b'103652' b'2596' b'438616'
b'242736' b'458128' b'336724' b'13416' b'37136' b'77544' b'4836' b'85268'
b'65564' b'4224' b'51224' b'10000' b'564916' b'41120' b'65464']
Feature: top_4_7_day_fresh_product
Tensor: [b'1260' b'2160' b'1264' b'79028' b'NA' b'4912' b'2124' b'1164' b'4828'
b'2316' b'156' b'32824' b'9232' b'2692' b'3188' b'93652' b'100' b'2204'
b'64836' b'4224' b'77684' b'176' b'9264' b'3176' b'14652' b'1376' b'2120'
b'3160' b'8248' b'1532' b'1448' b'3500' b'90068' b'6640' b'9856' b'2564'
b'1176' b'188' b'8520' b'2312' b'54124' b'1504' b'74448' b'2764' b'44'
b'52' b'3356' b'4744' b'1172' b'52096' b'110564' b'5048' b'438616'
b'1180' b'185620' b'77312' b'62604' b'361736' b'4452' b'1444' b'2116'
b'1192' b'5744' b'3504' b'11260' b'10920' b'65884' b'48' b'1340' b'6732'
b'94608' b'41428' b'52112' b'41352' b'13416' b'452352' b'6724' b'7920'
b'3184' b'41432' b'41324' b'2500' b'70372' b'4416' b'14468' b'11152'
b'3732' b'1372' b'65144' b'8328' b'49612' b'235772' b'7324' b'61244'
b'77516' b'66924' b'67940' b'33736' b'2720' b'78400' b'7732' b'98700'
b'41388' b'1156' b'452304' b'54148' b'259324' b'11792' b'49640' b'7480'
b'67956' b'787368' b'99560' b'65568' b'52140' b'108772' b'780612'
b'264088' b'6936' b'103652' b'49800' b'3080' b'452244' b'142956' b'76108'
b'5452' b'3168' b'988024' b'10000' b'71800' b'41312' b'88740' b'811008'
b'235472' b'65132' b'613360' b'34476' b'41756' b'67052' b'5568' b'74796'
b'188700' b'148984' b'214460' b'5724' b'78288' b'816568' b'144528'
b'59128' b'112436']
Feature: top_5_7_day_fresh_product
Tensor: [b'176' b'2320' b'2316' b'NA' b'9856' b'1372' b'66924' b'3080' b'44'
b'5552' b'2312' b'1260' b'452300' b'2564' b'3732' b'4744' b'110564'
b'1448' b'2764' b'41428' b'52' b'4452' b'148984' b'3356' b'8764' b'6640'
b'1264' b'156' b'4416' b'48' b'8520' b'2116' b'2692' b'3860' b'5996'
b'7128' b'11152' b'74448' b'10432' b'3160' b'61244' b'3188' b'3376'
b'188620' b'9264' b'77312' b'2124' b'1164' b'1192' b'555576' b'1532'
b'5568' b'839892' b'5692' b'2120' b'4912' b'4828' b'11792' b'1176'
b'90024' b'151976' b'77000' b'188' b'1444' b'14468' b'74780' b'41204'
b'7324' b'75564' b'59128' b'49808' b'188700' b'52192' b'3504' b'811008'
b'77684' b'173104' b'5092' b'5744' b'90660' b'67956' b'8248' b'65564'
b'140080' b'81896' b'52140' b'822276' b'777184' b'4224' b'62604' b'7732'
b'76108' b'93652' b'64836' b'92396' b'103652' b'2324' b'52112' b'66520'
b'79028' b'74796' b'41120' b'70376' b'66820' b'259324' b'3364' b'1504'
b'2720' b'10304' b'41452' b'41756' b'41432' b'41604' b'64132' b'3500'
b'787456' b'64572' b'811108' b'65144' b'12884' b'9844' b'71800' b'787256'
b'57704' b'7872' b'78400' b'1172' b'811024' b'49780' b'214460' b'6716'
b'2596' b'1376' b'3048' b'3184' b'41464' b'57692' b'90068' b'85792'
b'361736' b'811428' b'100' b'41716' b'66104' b'37120' b'71260' b'33652'
b'6936' b'51224' b'215984' b'102644' b'235472']
Feature: top_1_15_day_fresh_product
Tensor: [b'1260' b'1264' b'44' b'176' b'156' b'NA' b'66924' b'13416' b'8764'
b'811020' b'100' b'51224' b'438616' b'6936' b'1164' b'2312' b'458128'
b'52' b'1340' b'2116' b'2124' b'77312' b'7128' b'3500' b'2120' b'2316'
b'4744' b'4224' b'3376' b'1532' b'1180' b'555620' b'235772' b'4416'
b'5048' b'2764' b'188' b'67576' b'110564' b'1444' b'11792' b'3356'
b'59128' b'1376' b'11260' b'10920' b'949864' b'1192' b'90068' b'5092'
b'10884' b'87484' b'732924' b'49792' b'3316' b'74448' b'93652' b'41452'
b'103652' b'3160' b'48' b'67116' b'76108' b'49384' b'1172' b'66104'
b'811012' b'3080' b'3504' b'41428' b'3732' b'49480' b'452244' b'555576'
b'264088' b'41756' b'1504' b'2376' b'811076' b'2720' b'1448' b'37120'
b'3188' b'556188' b'64656' b'41768' b'2692' b'452304' b'5744' b'7324'
b'16296' b'62604' b'2564' b'4348' b'148984' b'8328' b'9264' b'141148'
b'49780' b'88576' b'4912' b'7732' b'1372' b'6716' b'123724' b'9856'
b'41120' b'3860' b'54124' b'5552' b'67956' b'52112' b'2160' b'10648'
b'57700' b'2500' b'2320' b'6640' b'49800' b'1176' b'57708' b'67940'
b'151976' b'8168' b'49600' b'188700' b'81764' b'119944' b'922492'
b'48012' b'78400' b'5712' b'52084' b'83576' b'361736' b'4452' b'41324'
b'65568' b'10656' b'6732' b'914204' b'6124' b'2596']
Feature: top_2_15_day_fresh_product
Tensor: [b'44' b'8520' b'176' b'1164' b'NA' b'188' b'1264' b'41428' b'100' b'1260'
b'811428' b'2764' b'2316' b'1444' b'2692' b'2116' b'41452' b'133116'
b'3080' b'9264' b'2312' b'156' b'4744' b'14468' b'110564' b'2564'
b'57776' b'10884' b'7732' b'1372' b'1448' b'3500' b'88576' b'8764'
b'79080' b'1532' b'1376' b'11152' b'452352' b'555748' b'10816' b'41608'
b'1176' b'1192' b'7324' b'12064' b'4224' b'77312' b'2124' b'4452'
b'41120' b'41772' b'3160' b'88740' b'2596' b'48' b'2320' b'1172' b'74448'
b'420336' b'6640' b'9856' b'52' b'67956' b'6724' b'77000' b'246112'
b'2120' b'93652' b'6936' b'3184' b'732952' b'811076' b'188620' b'945464'
b'49776' b'64128' b'41768' b'3732' b'49616' b'54124' b'1504' b'65564'
b'452252' b'48012' b'70372' b'1180' b'66924' b'41540' b'9272' b'8328'
b'49384' b'71800' b'76108' b'71260' b'3356' b'5712' b'67052' b'65964'
b'452244' b'811192' b'438616' b'6000' b'85104' b'41380' b'787368' b'3188'
b'235472' b'10000' b'121676' b'87344' b'66820' b'10920' b'62604' b'61244'
b'452304' b'12872' b'2720' b'64836' b'7480' b'49800' b'3376' b'4416'
b'2160' b'6124' b'71364' b'4912' b'65568' b'787256' b'235772' b'11596'
b'11752' b'3176' b'581832' b'90068' b'81188' b'3504' b'52192' b'2500'
b'1340' b'2376' b'59128' b'7128' b'57704' b'446792' b'717000' b'944936'
b'8248' b'41716' b'458128' b'60580' b'839892' b'79028' b'4348' b'64132'
b'5744' b'151976' b'41352' b'2324' b'67576' b'16296' b'214460' b'70316'
b'87976' b'41216' b'2204' b'34484' b'54160']
Feature: top_3_15_day_fresh_product
Tensor: [b'156' b'176' b'1260' b'NA' b'1164' b'1448' b'44' b'7732' b'2116'
b'811012' b'327444' b'41452' b'2764' b'33652' b'452300' b'1264' b'1376'
b'48' b'2312' b'2692' b'3188' b'8764' b'41120' b'90068' b'452352' b'2120'
b'3160' b'148984' b'1532' b'41428' b'93652' b'188' b'1192' b'52' b'100'
b'67116' b'3364' b'6716' b'3048' b'945464' b'1372' b'2124' b'49808'
b'74448' b'613360' b'76108' b'1172' b'556188' b'555784' b'88576' b'66308'
b'9856' b'11152' b'62604' b'6936' b'41716' b'41324' b'65132' b'1444'
b'4828' b'77312' b'3860' b'452212' b'4912' b'4416' b'5752' b'86520'
b'2316' b'59128' b'78348' b'8328' b'1180' b'3356' b'556808' b'3020'
b'37128' b'6724' b'2564' b'5712' b'4348' b'67956' b'922528' b'8520'
b'49792' b'173104' b'9272' b'64836' b'2500' b'9852' b'1504' b'555576'
b'71356' b'74796' b'3080' b'65144' b'48012' b'49456' b'3504' b'64132'
b'57700' b'78400' b'1176' b'9568' b'3176' b'5744' b'67576' b'245408'
b'458128' b'14468' b'57708' b'86852' b'110564' b'73008' b'2720' b'41604'
b'96956' b'54148' b'48780' b'810996' b'120284' b'61244' b'52188' b'41768'
b'37168' b'41380' b'32824' b'3184' b'99268' b'49800' b'452244' b'179940'
b'54124' b'5692' b'4452' b'4840' b'52140' b'2596' b'41156' b'73264'
b'90024' b'41204' b'87344' b'71284' b'555588' b'7324' b'37132' b'48772'
b'88740' b'3732' b'336724' b'3500' b'37136' b'77544' b'188700' b'121676'
b'4836' b'85268' b'66816' b'5552' b'2376' b'90852' b'8072' b'51224'
b'11352' b'5724' b'7128' b'57704' b'564916' b'37124']
Feature: top_4_15_day_fresh_product
Tensor: [b'3860' b'2160' b'1264' b'1448' b'1260' b'NA' b'4452' b'74448' b'1164'
b'811944' b'13416' b'452324' b'1444' b'3500' b'41756' b'41608' b'452352'
b'93652' b'176' b'2124' b'11152' b'8764' b'44' b'4416' b'90024' b'3048'
b'2500' b'1372' b'2764' b'3176' b'2120' b'2116' b'3160' b'90068'
b'811772' b'1192' b'156' b'462508' b'1532' b'3184' b'110564' b'188' b'52'
b'14468' b'112436' b'2312' b'52096' b'3504' b'76108' b'66816' b'65964'
b'78348' b'438616' b'5048' b'2564' b'52188' b'3188' b'1376' b'2692'
b'2316' b'4224' b'41120' b'79028' b'1176' b'6936' b'1172' b'5744'
b'16344' b'64132' b'3080' b'9272' b'41768' b'6128' b'49784' b'100' b'48'
b'5752' b'62604' b'41428' b'1340' b'246112' b'49800' b'839892' b'66308'
b'2204' b'264088' b'10920' b'41324' b'37132' b'811964' b'49788' b'41604'
b'71800' b'1504' b'7872' b'3732' b'7732' b'78400' b'49612' b'4744'
b'7324' b'54124' b'6640' b'452304' b'811120' b'2320' b'41216' b'77516'
b'3356' b'4912' b'54148' b'555576' b'48012' b'7128' b'67116' b'74796'
b'4828' b'79064' b'64572' b'452244' b'9568' b'8328' b'452220' b'61244'
b'67268' b'49596' b'811024' b'787368' b'64764' b'57692' b'65564' b'8168'
b'605148' b'65144' b'103652' b'121676' b'11260' b'54160' b'76348' b'1180'
b'10000' b'64128' b'66924' b'811952' b'959780' b'2720' b'70136' b'16336'
b'811008' b'16280' b'235772' b'241064' b'49492' b'2560' b'452240'
b'337124' b'67956' b'429736' b'5568' b'9264' b'214460' b'922528'
b'151976' b'816568' b'144528' b'4200']
Feature: top_5_15_day_fresh_product
Tensor: [b'3500' b'2320' b'2316' b'2120' b'1264' b'NA' b'1372' b'4912' b'64132'
b'2312' b'14468' b'176' b'839892' b'2124' b'2116' b'3732' b'41452'
b'54148' b'62604' b'188' b'3356' b'327444' b'48012' b'9264' b'3176'
b'10816' b'1192' b'3160' b'4828' b'1164' b'156' b'66308' b'52' b'3860'
b'452244' b'100' b'1260' b'2564' b'1176' b'41432' b'1180' b'1532' b'2376'
b'54124' b'48' b'2692' b'3184' b'44' b'7324' b'77808' b'10920' b'85268'
b'361736' b'1172' b'41120' b'1444' b'1448' b'4744' b'3188' b'3080'
b'6936' b'88740' b'151976' b'2764' b'3504' b'3048' b'65884' b'75756'
b'76108' b'74448' b'103652' b'2500' b'1340' b'59128' b'49808' b'41464'
b'71052' b'52084' b'1376' b'8248' b'452352' b'7920' b'16312' b'67116'
b'70940' b'86792' b'188700' b'52140' b'188620' b'556808' b'235772'
b'7732' b'5996' b'61244' b'47984' b'92396' b'2560' b'3364' b'120284'
b'5552' b'2324' b'79028' b'52112' b'33736' b'2720' b'71260' b'74796'
b'13416' b'57776' b'41604' b'66820' b'93652' b'11152' b'10304' b'4416'
b'452304' b'259324' b'11792' b'787456' b'70448' b'64128' b'77516' b'8764'
b'780612' b'52192' b'110564' b'57704' b'811192' b'41428' b'78400'
b'49796' b'67044' b'4224' b'142956' b'3168' b'41716' b'64536' b'5692'
b'185620' b'90068' b'235472' b'78132' b'1504' b'438616' b'5048' b'77312'
b'64656' b'67956' b'840108' b'7128' b'41776' b'6724' b'65464' b'68224']
Feature: top_1_60_day_fresh_product
Tensor: [b'1260' b'1264' b'44' b'176' b'8536' b'66924' b'184' b'8764' b'811020'
b'100' b'1164' b'438616' b'2116' b'2692' b'41608' b'49600' b'2312' b'156'
b'NA' b'1340' b'52108' b'5716' b'6640' b'4416' b'77312' b'9264' b'7128'
b'3500' b'1376' b'76108' b'4224' b'1172' b'1180' b'73260' b'98784'
b'1192' b'9856' b'7324' b'3160' b'12064' b'5048' b'452244' b'613076'
b'70376' b'48' b'188' b'452212' b'110564' b'3356' b'2124' b'2120' b'52'
b'1444' b'41464' b'59128' b'77000' b'5552' b'246112' b'556808' b'65884'
b'74448' b'2500' b'1532' b'57700' b'2564' b'41432' b'732952' b'49792'
b'3316' b'41452' b'67116' b'49788' b'3080' b'48012' b'6724' b'2764'
b'3504' b'41428' b'49480' b'2560' b'74796' b'2316' b'41756' b'1504'
b'6832' b'811076' b'2720' b'1176' b'556188' b'14672' b'41768' b'65964'
b'452304' b'16296' b'62604' b'4348' b'41324' b'41120' b'34484' b'71260'
b'5744' b'96956' b'61244' b'3048' b'49780' b'461668' b'103652' b'52188'
b'1448' b'65568' b'37128' b'49596' b'64132' b'67956' b'811024' b'6716'
b'65144' b'1372' b'3184' b'71800' b'9272' b'529128' b'54124' b'452368'
b'811008' b'49808' b'8168' b'51224' b'151976' b'60580' b'2596' b'88740'
b'78400' b'2160' b'839892' b'83576' b'5712' b'52084' b'361736' b'66104'
b'10920' b'90068' b'5724' b'914204' b'6936' b'144528' b'4200']
Feature: top_2_60_day_fresh_product
Tensor: [b'44' b'176' b'1264' b'1260' b'NA' b'156' b'2560' b'11596' b'100' b'1164'
b'811428' b'2316' b'2596' b'41452' b'133116' b'65884' b'65464' b'2116'
b'2124' b'2764' b'4744' b'64572' b'93652' b'8244' b'88740' b'3500'
b'2564' b'179940' b'7732' b'74448' b'6716' b'3048' b'3176' b'2120'
b'8764' b'9856' b'4416' b'1532' b'3376' b'2312' b'1448' b'14672' b'65568'
b'5048' b'54148' b'70940' b'49808' b'4224' b'77312' b'7324' b'5752'
b'41120' b'564896' b'1172' b'67576' b'142956' b'3160' b'6936' b'811944'
b'3504' b'57692' b'188' b'65128' b'8328' b'2320' b'1176' b'52' b'85104'
b'4828' b'52140' b'5712' b'10884' b'87484' b'811076' b'67940' b'86520'
b'246112' b'452304' b'1192' b'839892' b'7128' b'48' b'148984' b'1372'
b'1504' b'41324' b'2720' b'811012' b'49788' b'110564' b'52188' b'66924'
b'3188' b'49388' b'452244' b'52156' b'9272' b'121676' b'41756' b'2500'
b'41428' b'52096' b'82408' b'3080' b'37120' b'66820' b'67052' b'438616'
b'245408' b'2692' b'86852' b'4348' b'4452' b'1444' b'67116' b'3184'
b'65564' b'64128' b'3364' b'9264' b'10920' b'26792' b'82868' b'452324'
b'90068' b'49596' b'66308' b'52108' b'7480' b'810996' b'88576' b'4912'
b'77652' b'65144' b'1180' b'3356' b'81676' b'581832' b'51224' b'2324'
b'81188' b'52192' b'5996' b'2376' b'184' b'6724' b'2204' b'446792'
b'76348' b'107676' b'49780' b'49800' b'103652' b'8168' b'64764' b'48012'
b'41432' b'79028' b'5552' b'49604' b'452300' b'5092' b'82940' b'811000'
b'62604' b'3732' b'76108' b'57776' b'66104' b'6124']
Feature: top_3_60_day_fresh_product
Tensor: [b'156' b'2312' b'1260' b'44' b'100' b'NA' b'176' b'5744' b'4348' b'1164'
b'811012' b'3160' b'41452' b'1376' b'33652' b'52140' b'2116' b'1264'
b'77312' b'7872' b'66308' b'458128' b'1172' b'2316' b'74448' b'3364'
b'14468' b'141704' b'188' b'1372' b'2764' b'4416' b'246112' b'12064'
b'1532' b'88576' b'4224' b'811020' b'48784' b'4828' b'49808' b'1180'
b'9264' b'52' b'452352' b'2564' b'73264' b'2500' b'148984' b'3504'
b'1192' b'6124' b'3188' b'6936' b'41716' b'2124' b'839892' b'564912'
b'41380' b'7128' b'949664' b'62604' b'90676' b'556808' b'11908' b'6724'
b'11260' b'77000' b'48' b'7324' b'8764' b'2320' b'77808' b'2120' b'67956'
b'1444' b'71052' b'6716' b'8240' b'5048' b'188620' b'71800' b'41428'
b'41540' b'3080' b'1448' b'847168' b'64128' b'41768' b'54124' b'1408'
b'2692' b'452252' b'70372' b'71356' b'49472' b'90068' b'70424' b'264088'
b'8520' b'65144' b'3184' b'2560' b'2376' b'11596' b'6640' b'4744' b'3168'
b'81188' b'73008' b'811192' b'67576' b'6000' b'74796' b'85104' b'57776'
b'787368' b'452304' b'121676' b'64132' b'87344' b'2720' b'88740' b'3356'
b'41604' b'9272' b'8328' b'67940' b'54148' b'3732' b'49596' b'811024'
b'555576' b'1504' b'452220' b'65568' b'787256' b'1176' b'52192' b'110564'
b'9856' b'57692' b'49800' b'65548' b'59128' b'41756' b'49488' b'5752'
b'3048' b'2596' b'76108' b'5552' b'41732' b'65464' b'93652' b'66924'
b'904620' b'57700' b'7732' b'811772' b'452240' b'65296' b'48772' b'81764'
b'151976' b'64764' b'3176' b'49792' b'33644' b'83576' b'2160' b'41464'
b'605148' b'6732' b'41216' b'34484' b'54160']
Feature: top_4_60_day_fresh_product
Tensor: [b'176' b'8520' b'1264' b'NA' b'1260' b'2692' b'1372' b'13416' b'188'
b'2116' b'811944' b'1164' b'41768' b'1444' b'3080' b'156' b'1448' b'48'
b'52' b'5048' b'77312' b'100' b'2120' b'44' b'74448' b'4452' b'2764'
b'2312' b'2500' b'3160' b'581832' b'10884' b'1532' b'3364' b'452240'
b'41428' b'462508' b'1176' b'438616' b'62604' b'14468' b'2316' b'3176'
b'246112' b'11152' b'2124' b'9264' b'1192' b'3504' b'110564' b'41324'
b'2564' b'5744' b'179940' b'41540' b'6936' b'1376' b'67956' b'65884'
b'1172' b'88740' b'41216' b'79064' b'76108' b'59128' b'37132' b'3184'
b'10432' b'9272' b'732924' b'4744' b'71264' b'452304' b'49776' b'7128'
b'839900' b'184' b'8764' b'78400' b'66104' b'41432' b'6320' b'48636'
b'4416' b'7872' b'8328' b'49600' b'49816' b'3732' b'79636' b'77544'
b'79028' b'41196' b'41120' b'66308' b'9844' b'4828' b'41604' b'452212'
b'7732' b'3860' b'185620' b'70376' b'74796' b'90068' b'839892' b'3500'
b'65548' b'93652' b'3356' b'3188' b'6732' b'86792' b'452220' b'1180'
b'11792' b'9568' b'3168' b'52140' b'845268' b'41716' b'61244' b'11752'
b'11908' b'6124' b'8168' b'79080' b'5552' b'41172' b'52156' b'613360'
b'108772' b'564916' b'64772' b'5692' b'86852' b'52112' b'2560' b'103652'
b'64128' b'121676' b'5996' b'2720' b'605420' b'2596' b'49784' b'3048'
b'555576' b'10648' b'87344' b'11596' b'33708' b'810996' b'5860' b'57708'
b'88576' b'49388' b'188700' b'119944' b'2320' b'67116' b'51224' b'6832'
b'811076' b'151976' b'214460' b'97240' b'84120' b'92396' b'816568'
b'78388' b'1504' b'37124' b'8244']
Feature: top_5_60_day_fresh_product
Tensor: [b'1264' b'156' b'2316' b'1164' b'44' b'NA' b'188' b'2120' b'64572'
b'1172' b'1260' b'78388' b'1448' b'65144' b'6936' b'2312' b'140080'
b'2764' b'176' b'64836' b'4416' b'13416' b'3048' b'3500' b'7324' b'2692'
b'5752' b'2124' b'3364' b'100' b'6640' b'2116' b'51224' b'90068' b'9856'
b'48' b'41812' b'1192' b'41432' b'74448' b'452244' b'52' b'1532'
b'452300' b'5552' b'1176' b'52096' b'62604' b'438616' b'4452' b'66816'
b'41732' b'3188' b'2596' b'41120' b'3160' b'64128' b'1376' b'1444'
b'811052' b'11792' b'3356' b'245720' b'71264' b'1180' b'70372' b'2564'
b'76108' b'70176' b'77544' b'811000' b'41756' b'41428' b'945464'
b'787256' b'1372' b'581832' b'5048' b'3080' b'3184' b'5996' b'3504'
b'3732' b'264088' b'452324' b'49384' b'9272' b'41708' b'9852' b'86792'
b'41604' b'4828' b'5716' b'14468' b'41540' b'11260' b'59128' b'67940'
b'88740' b'8764' b'3860' b'93652' b'41716' b'103652' b'2320' b'839900'
b'64132' b'4348' b'65084' b'5744' b'52140' b'6124' b'2720' b'78400'
b'41380' b'787456' b'67116' b'206252' b'3176' b'8328' b'452220' b'452304'
b'66308' b'2308' b'556188' b'65296' b'603460' b'811968' b'65384' b'57700'
b'603440' b'65564' b'8248' b'64968' b'41768' b'71800' b'65108' b'7732'
b'605148' b'71260' b'77312' b'4912' b'246112' b'16344' b'9264' b'811428'
b'79028' b'67956' b'76348' b'5692' b'7872' b'49804' b'845268' b'2500'
b'2160' b'839892' b'37172' b'4744' b'110564' b'41724' b'109768' b'185620'
b'12356' b'10920' b'1340' b'377356' b'86520' b'458128' b'54124' b'49600'
b'12872' b'33652' b'41216' b'4224' b'78432' b'86852' b'607836' b'65568'
b'70304' b'277204' b'5712' b'74796' b'6724' b'77516' b'2560']
Feature: top_1_90_day_fresh_product
Tensor: [b'1260' b'1264' b'44' b'66924' b'176' b'77000' b'11596' b'811020' b'100'
b'1164' b'438616' b'41452' b'65884' b'49600' b'2312' b'2116' b'1376'
b'NA' b'8764' b'1340' b'2500' b'5716' b'3500' b'6640' b'4416' b'77312'
b'12064' b'9264' b'74448' b'7128' b'156' b'52' b'76108' b'4224' b'1172'
b'1180' b'14672' b'98784' b'9856' b'7324' b'70940' b'452244' b'613076'
b'564896' b'2316' b'48788' b'2124' b'102640' b'110564' b'3356' b'2120'
b'2764' b'1444' b'59128' b'5552' b'246112' b'1448' b'3160' b'1532'
b'57704' b'2564' b'41432' b'732952' b'49792' b'1192' b'3316' b'839892'
b'67116' b'48012' b'6724' b'2204' b'3504' b'41428' b'49480' b'2560'
b'41756' b'1372' b'1504' b'6832' b'2720' b'1176' b'556188' b'48'
b'452304' b'16296' b'4452' b'62604' b'4348' b'41324' b'41120' b'188'
b'34484' b'88740' b'96956' b'61244' b'11792' b'49780' b'5744' b'461668'
b'3184' b'103652' b'52188' b'77652' b'49596' b'8328' b'67956' b'811428'
b'6716' b'3080' b'65144' b'2376' b'57700' b'71800' b'9272' b'529128'
b'54124' b'811008' b'86520' b'52140' b'8168' b'88576' b'2596' b'78400'
b'41768' b'2160' b'52084' b'83576' b'361736' b'66104' b'10920' b'90068'
b'914204' b'54160' b'6936' b'144528' b'4200']
Feature: top_2_90_day_fresh_product
Tensor: [b'44' b'176' b'1260' b'2204' b'156' b'1264' b'3364' b'8764' b'13416'
b'1164' b'811012' b'2316' b'2596' b'2692' b'140080' b'65464' b'NA'
b'2764' b'4744' b'64572' b'93652' b'3048' b'52108' b'8244' b'2564'
b'108772' b'2124' b'581832' b'7732' b'3500' b'1192' b'74448' b'4224'
b'4828' b'2120' b'9856' b'4416' b'1532' b'1448' b'2116' b'73264' b'3080'
b'49808' b'2312' b'839892' b'12872' b'48784' b'564912' b'100' b'41432'
b'188' b'67576' b'142956' b'3160' b'6936' b'4348' b'86520' b'57692'
b'59128' b'556808' b'78348' b'3504' b'11260' b'52' b'85104' b'52140'
b'6732' b'5712' b'10884' b'1172' b'71052' b'732924' b'811076' b'452352'
b'71800' b'780612' b'1376' b'77312' b'1372' b'148984' b'41324' b'37132'
b'246112' b'52188' b'3188' b'49388' b'74796' b'52156' b'9272' b'8328'
b'121676' b'1180' b'2500' b'41428' b'2320' b'82408' b'37120' b'66820'
b'110564' b'14672' b'185620' b'67052' b'5448' b'70412' b'86852' b'9264'
b'787368' b'452304' b'66924' b'65564' b'41120' b'64128' b'11792' b'1504'
b'88740' b'5744' b'452212' b'82868' b'3184' b'452324' b'90068' b'14468'
b'49596' b'48' b'48776' b'1444' b'98700' b'65144' b'6124' b'1176'
b'922492' b'65568' b'52192' b'51224' b'605420' b'79028' b'67956' b'5048'
b'81188' b'70740' b'2720' b'184' b'811008' b'6724' b'7128' b'41732'
b'446792' b'107676' b'438616' b'6640' b'810996' b'76108' b'71260'
b'151976' b'52096' b'64764' b'60580' b'7324' b'3176' b'49604' b'57700'
b'5092' b'52084' b'82940' b'62604' b'41352' b'41756' b'5552' b'41452'
b'57776' b'84120' b'41216' b'10920' b'5724']
Feature: top_3_90_day_fresh_product
Tensor: [b'156' b'2312' b'1260' b'1264' b'176' b'1164' b'5744' b'1372' b'85104'
b'77000' b'811428' b'3160' b'41452' b'33652' b'2116' b'77312' b'44'
b'1448' b'7872' b'458128' b'2564' b'2124' b'NA' b'1172' b'3364' b'67940'
b'14468' b'141704' b'188' b'88740' b'246112' b'100' b'2596' b'811020'
b'3504' b'49808' b'74448' b'1376' b'37132' b'452352' b'2764' b'1532'
b'73260' b'9264' b'54148' b'2500' b'5996' b'4416' b'1192' b'6936' b'2120'
b'564952' b'7128' b'949664' b'62604' b'90676' b'82408' b'6640' b'41464'
b'3048' b'1444' b'76108' b'8328' b'1180' b'2692' b'52' b'8764' b'48'
b'77808' b'67956' b'9272' b'2316' b'87484' b'6716' b'65548' b'452304'
b'93652' b'3080' b'67116' b'64128' b'148984' b'452324' b'1504' b'1408'
b'65084' b'86792' b'49788' b'70372' b'110564' b'59128' b'49472' b'7732'
b'65564' b'90068' b'264088' b'65144' b'41756' b'71800' b'52096' b'2376'
b'41120' b'839900' b'438616' b'81188' b'103652' b'73008' b'9568'
b'185620' b'74796' b'64132' b'87344' b'7324' b'235472' b'71260' b'3500'
b'10920' b'68100' b'3356' b'41604' b'2720' b'41428' b'52108' b'3188'
b'88576' b'4912' b'3376' b'41716' b'4828' b'452220' b'65568' b'9856'
b'10652' b'581832' b'4224' b'3176' b'11908' b'98784' b'117632' b'4744'
b'57692' b'68104' b'52156' b'2204' b'2320' b'3184' b'452368' b'49780'
b'57700' b'65296' b'81764' b'41432' b'4348' b'5552' b'66104' b'452300'
b'51224' b'121676' b'83576' b'2160' b'811024' b'555576' b'34484' b'6124']
Feature: top_4_90_day_fresh_product
Tensor: [b'176' b'1192' b'1264' b'100' b'49808' b'1164' b'74448' b'44' b'2120'
b'214460' b'156' b'85104' b'811944' b'41768' b'1376' b'2692' b'3080'
b'1260' b'52140' b'2116' b'48' b'188' b'52' b'2312' b'7732' b'NA' b'2764'
b'5048' b'235472' b'3364' b'90068' b'3500' b'7324' b'4416' b'1372'
b'14468' b'2316' b'10884' b'1532' b'6716' b'9856' b'3048' b'3160'
b'462508' b'438616' b'1180' b'5744' b'9264' b'1444' b'3376' b'2564'
b'14652' b'3176' b'1176' b'65568' b'41224' b'246112' b'11152' b'2124'
b'1448' b'5752' b'3188' b'70376' b'77312' b'41120' b'1172' b'78432'
b'4452' b'245720' b'65128' b'65884' b'787256' b'79064' b'8520' b'4912'
b'2204' b'70176' b'8240' b'4744' b'71264' b'49776' b'7128' b'452304'
b'839892' b'3504' b'5092' b'9272' b'54124' b'2500' b'66104' b'110564'
b'41432' b'2720' b'6320' b'452252' b'7872' b'92116' b'66924' b'121676'
b'49600' b'8244' b'452244' b'70424' b'65564' b'76108' b'3184' b'5568'
b'2560' b'41324' b'65964' b'3732' b'6000' b'4828' b'74796' b'57776'
b'41716' b'3168' b'8764' b'65548' b'811940' b'2596' b'6936' b'839900'
b'452220' b'9568' b'8328' b'555576' b'6732' b'67956' b'61244' b'65384'
b'93652' b'88740' b'41540' b'79080' b'41172' b'52156' b'251052' b'148984'
b'37128' b'811192' b'49800' b'64132' b'67044' b'2324' b'78400' b'14672'
b'49784' b'52108' b'5996' b'41428' b'555588' b'344784' b'5552' b'264088'
b'10656' b'64020' b'11596' b'33708' b'452352' b'62604' b'5860' b'57708'
b'811772' b'48028' b'564916' b'48772' b'49788' b'377356' b'556808'
b'581832' b'65144' b'64764' b'8248' b'151976' b'2320' b'97240' b'1504'
b'452300' b'107604' b'78388' b'37124']
Feature: top_5_90_day_fresh_product
Tensor: [b'1264' b'2316' b'176' b'44' b'1172' b'1260' b'7732' b'188' b'100'
b'64132' b'78388' b'1444' b'156' b'6936' b'2120' b'65084' b'2312' b'1164'
b'133116' b'41608' b'NA' b'93652' b'77312' b'74448' b'4452' b'2764' b'52'
b'67116' b'2124' b'3160' b'2692' b'3504' b'4416' b'179940' b'2116'
b'1372' b'1192' b'48' b'1176' b'1532' b'3080' b'452244' b'1376' b'452240'
b'5552' b'52096' b'5744' b'6124' b'41120' b'41324' b'564916' b'1448'
b'3364' b'64128' b'5752' b'811944' b'2564' b'3048' b'1180' b'3184'
b'3500' b'2560' b'76108' b'57700' b'4828' b'52108' b'64656' b'4348'
b'41716' b'452304' b'3860' b'41216' b'581832' b'811000' b'184' b'3188'
b'3732' b'11596' b'78400' b'557876' b'41708' b'438616' b'5692' b'49788'
b'2160' b'48636' b'86520' b'2320' b'8328' b'2500' b'71356' b'67940'
b'8764' b'79028' b'41196' b'5996' b'51224' b'811076' b'54124' b'66308'
b'9844' b'77244' b'41604' b'92396' b'7128' b'5860' b'90068' b'71792'
b'33724' b'3176' b'3356' b'1504' b'65564' b'41428' b'768752' b'54148'
b'839900' b'52140' b'810996' b'13416' b'65548' b'811772' b'11752'
b'78432' b'199836' b'452300' b'48648' b'64968' b'787256' b'67576'
b'41768' b'6640' b'246112' b'74796' b'59128' b'41756' b'108772' b'1156'
b'6716' b'839892' b'2596' b'49488' b'49804' b'845268' b'206252' b'26792'
b'110564' b'9568' b'41224' b'603460' b'65464' b'41724' b'185620' b'88576'
b'88740' b'10920' b'70176' b'377356' b'811428' b'8168' b'149008' b'49388'
b'12872' b'65144' b'33652' b'151976' b'49816' b'48012' b'14468' b'4744'
b'6832' b'33644' b'41432' b'71260' b'214460' b'605148' b'41204' b'816568'
b'77516' b'8244']
Feature: top_1_7_day_non_fresh_product
Tensor: [b'2192' b'124' b'1352' b'1356' b'1228' b'968504' b'8216' b'7424' b'1528'
b'736296' b'245488' b'67624' b'51504' b'5616' b'59608' b'88' b'9956'
b'40456' b'1332' b'2496' b'64616' b'3460' b'66460' b'11424' b'80460'
b'2556' b'8500' b'10104' b'455536' b'1380' b'399060' b'19740' b'1276'
b'4648' b'16696' b'37752' b'5344' b'28' b'1232' b'1560' b'2492' b'1184'
b'45244' b'2572' b'771148' b'2412' b'60928' b'24' b'2860' b'NA' b'1516'
b'4708' b'211568' b'2140' b'12672' b'1284' b'44796' b'2732' b'31068'
b'1240' b'1424' b'1324' b'1452' b'4408' b'116796' b'2676' b'7152' b'3060'
b'809012' b'14828' b'7108' b'64088' b'20' b'455204' b'48688' b'4748'
b'36584' b'53648' b'118652' b'313612' b'210984' b'8388' b'3056' b'2188'
b'4696' b'16600' b'1304' b'2776' b'25136' b'36328' b'75152' b'6348'
b'57152' b'6156' b'25128' b'300016' b'1344' b'92344' b'8104' b'70340'
b'6564' b'25108' b'70516' b'1328' b'7668' b'36568' b'1220' b'7224'
b'8592' b'4104' b'3124' b'2132' b'116452' b'4328' b'3808' b'24816'
b'915816' b'135372' b'51292' b'277808' b'49712' b'160' b'50552' b'2296'
b'326180' b'16680' b'3532' b'2704' b'4160' b'583488' b'6356' b'1512'
b'370204' b'4464' b'3976' b'3488' b'2488' b'6364' b'3596' b'1312'
b'36504' b'818192' b'50636' b'4948' b'2568' b'2584' b'108' b'4564'
b'84160' b'10756' b'65932' b'48664' b'2456' b'4924' b'12552' b'5388'
b'490528' b'5672' b'70292' b'65812' b'3092' b'84652' b'956856' b'32'
b'3200' b'2416' b'10808' b'51448' b'8556' b'41872' b'90236' b'557064'
b'1216' b'447028' b'75124' b'84372' b'3236' b'3744' b'65428' b'1248'
b'66564' b'9416' b'147824' b'32856' b'12136' b'2260' b'293556' b'11632'
b'33180' b'33412' b'49756' b'90296' b'4016' b'4780' b'2552' b'3668'
b'764628' b'26508' b'6944' b'1500' b'16668' b'3276' b'5596' b'45612'
b'7176' b'216276' b'63380' b'5080' b'6612' b'95460' b'44480' b'74684'
b'219788' b'7980' b'6052' b'11008' b'64228' b'25132' b'2112' b'2148'
b'12556' b'4816' b'36444' b'15604' b'2240' b'95320' b'51864' b'3920'
b'450356' b'989708' b'11836' b'77380' b'9544' b'957296' b'11732' b'10200'
b'557748' b'908208' b'77908' b'57148' b'28376' b'168' b'103520' b'36852'
b'14796' b'3784' b'1404' b'94716' b'103884' b'84' b'4196' b'251808' b'72'
b'51620' b'92284' b'24980' b'4812' b'60944' b'2508' b'2256' b'9884'
b'3484' b'83740' b'3720' b'7736' b'501784' b'83872' b'6512' b'776584'
b'76' b'14480' b'136' b'961820' b'4192' b'6212' b'2280' b'64824' b'12204'
b'16168' b'6748' b'3468' b'1360' b'113520' b'12532' b'10428' b'765808'
b'8704' b'12176' b'4704' b'89560' b'62252' b'11432' b'7960' b'6664'
b'5444' b'142452' b'5636' b'3144' b'5944' b'2688' b'1224' b'24836'
b'3564' b'6380' b'17784' b'15920' b'3344' b'5688' b'2348' b'2208'
b'51556' b'114072' b'68460' b'17528' b'3324' b'1552' b'17596' b'119164'
b'65424' b'48608' b'8644' b'597344' b'65968' b'903488' b'12436' b'1252'
b'3736' b'1368' b'52220' b'2848' b'95916' b'92164' b'65012' b'12544'
b'7228' b'73408' b'2332' b'214472' b'77936' b'286488' b'68' b'31060'
b'989268' b'1384' b'13636' b'373576' b'121212' b'1212' b'455356' b'44860'
b'5508' b'40396' b'7712' b'2268' b'379780' b'3028' b'5404' b'14524'
b'2304' b'24112' b'1556' b'65696' b'97332' b'6632' b'6816' b'10424'
b'4880' b'33276' b'87020' b'5704' b'200112' b'273364' b'54988' b'454264'
b'113336' b'6072' b'24932' b'96944' b'9132' b'2264' b'83596' b'64112'
b'4520' b'1256' b'2248' b'6524' b'38512' b'22320' b'2520' b'25384'
b'55156' b'2180' b'11052' b'8808' b'111148' b'809428' b'4560' b'5236'
b'91612' b'306644' b'107352' b'2532']
Feature: top_2_7_day_non_fresh_product
Tensor: [b'51504' b'51716' b'65436' b'1352' b'2456' b'2492' b'1356' b'23944'
b'2188' b'2192' b'5316' b'1764' b'4240' b'10104' b'1312' b'609960' b'124'
b'22260' b'65932' b'2132' b'14804' b'557064' b'7668' b'57152' b'1424'
b'8500' b'82400' b'1620' b'3600' b'28' b'1500' b'12556' b'2688' b'4816'
b'2572' b'12176' b'2180' b'25108' b'68484' b'3668' b'2140' b'14756'
b'17756' b'1560' b'4148' b'14764' b'2848' b'16800' b'173436' b'6668'
b'114296' b'9344' b'2584' b'40456' b'1276' b'1512' b'6844' b'1184'
b'3200' b'88' b'65828' b'1452' b'NA' b'149492' b'5080' b'48736' b'1360'
b'8592' b'16132' b'44872' b'36764' b'19740' b'495964' b'31116' b'1284'
b'4908' b'68' b'1588' b'4648' b'8216' b'1228' b'64136' b'65052' b'3092'
b'1220' b'1612' b'7736' b'122012' b'66564' b'2520' b'6612' b'3276'
b'3144' b'9308' b'42444' b'6348' b'2352' b'24' b'2296' b'4748' b'60856'
b'3824' b'66088' b'44480' b'917780' b'11928' b'83900' b'10808' b'2516'
b'968008' b'2416' b'2440' b'749928' b'11468' b'2412' b'951152' b'7108'
b'1188' b'293556' b'68496' b'51444' b'764100' b'2148' b'4220' b'5084'
b'399060' b'4520' b'3744' b'1272' b'596104' b'5136' b'8180' b'89028'
b'20' b'300016' b'4524' b'10036' b'12136' b'45572' b'78504' b'2704'
b'455536' b'98044' b'25132' b'49712' b'49732' b'3124' b'49760' b'2208'
b'4408' b'1556' b'2176' b'28840' b'71452' b'3056' b'4276' b'5616'
b'64680' b'61112' b'10756' b'31068' b'908208' b'4192' b'1344' b'19172'
b'4572' b'64088' b'1368' b'6848' b'1232' b'4812' b'105632' b'56' b'2260'
b'454264' b'36444' b'73076' b'54988' b'4000' b'4172' b'2676' b'67028'
b'82364' b'76200' b'3084' b'1212' b'5056' b'1464' b'5348' b'65428'
b'3516' b'2144' b'6352' b'1468' b'89688' b'36568' b'82244' b'11336'
b'70516' b'83740' b'2860' b'3196' b'4564' b'2672' b'227156' b'8868'
b'2508' b'12656' b'1536' b'66796' b'488608' b'25772' b'51620' b'173052'
b'37928' b'4104' b'1404' b'2556' b'4196' b'597420' b'36852' b'12272'
b'502228' b'65708' b'10896' b'8556' b'27232' b'11024' b'6356' b'2332'
b'10992' b'94712' b'10064' b'11924' b'76' b'565772' b'7912' b'66460'
b'785820' b'22160' b'184120' b'108936' b'2248' b'43932' b'8388' b'66448'
b'14628' b'11512' b'3660' b'5960' b'6568' b'100696' b'7880' b'32'
b'475956' b'2304' b'65816' b'66568' b'1332' b'1248' b'5704' b'80116'
b'61064' b'823960' b'28368' b'908244' b'51292' b'65836' b'71428' b'2388'
b'172' b'41872' b'76312' b'16600' b'2540' b'105900' b'816152' b'11236'
b'20180' b'7224' b'12848' b'1244' b'3028' b'36824' b'21824' b'777220'
b'12360' b'3344' b'14532' b'242756' b'51424' b'82604' b'64852' b'9956'
b'3596' b'444016' b'5688' b'4040' b'784484' b'1436' b'9872' b'2624'
b'12580' b'300012' b'955756' b'5116' b'3460' b'22916' b'176580' b'1324'
b'1488' b'64056' b'4292' b'58312' b'36848' b'24984' b'42608' b'44884'
b'988636' b'35988' b'108' b'11376' b'66956' b'5236' b'2512' b'66952'
b'145892' b'63996' b'71440' b'761392' b'489120' b'51756' b'11200'
b'530892' b'2484' b'43976' b'65664' b'71004' b'24816' b'40432' b'76144'
b'1396' b'50876' b'48600' b'25128' b'5508' b'8960' b'1584' b'54976'
b'70220' b'3236' b'10224' b'21512' b'12544' b'5636' b'10184' b'1328'
b'57148' b'65916' b'49180' b'55380' b'7984' b'83132' b'3868' b'16536'
b'19748' b'35452' b'964104' b'6132' b'65424' b'64824' b'916296' b'1240'
b'108712' b'16748' b'66348' b'5444' b'12184' b'3524' b'43108' b'214408'
b'91696' b'5232' b'11688' b'45204' b'48624' b'78848' b'43384' b'7492'
b'7744' b'24784' b'1320' b'5592' b'51448' b'601476' b'3284' b'65684'
b'70292' b'83460' b'9276' b'746308' b'70340' b'4936' b'4380' b'2348'
b'375392' b'36912' b'42912' b'12380' b'93044' b'17596' b'67244' b'9544'
b'551076' b'15920' b'2212' b'2532' b'54436' b'2732' b'4948' b'3108'
b'750428' b'753992' b'6364' b'3476' b'144788' b'5984' b'36328' b'122672'
b'1528' b'64664' b'1420' b'9408' b'84928' b'443368' b'24788' b'24116'
b'5736' b'3920' b'1572' b'5676' b'203036' b'2292' b'79408' b'16' b'5332'
b'34880' b'6156' b'8124' b'6452' b'3956' b'65812' b'279408' b'273356'
b'537452' b'4160' b'3736' b'1460' b'737736' b'53684' b'6276' b'683160'
b'46988' b'144' b'10892' b'1304' b'12704' b'24836' b'51752' b'65904'
b'11880' b'3488' b'49664' b'7176' b'49448' b'3060' b'42612' b'819108'
b'12436' b'6224' b'24940' b'120' b'49452' b'4016' b'3480' b'16696'
b'2232']
Feature: top_3_7_day_non_fresh_product
Tensor: [b'3056' b'1284' b'86780' b'119632' b'118652' b'2624' b'2332' b'66312'
b'2572' b'2732' b'41872' b'5736' b'1368' b'70312' b'51580' b'1312'
b'66564' b'70516' b'1232' b'25108' b'65444' b'62080' b'965524' b'5616'
b'1248' b'4220' b'66108' b'1352' b'6632' b'4816' b'1424' b'81096' b'2132'
b'NA' b'24284' b'1356' b'11200' b'8500' b'54524' b'8960' b'2776' b'2492'
b'1184' b'34580' b'5104' b'5316' b'2704' b'30292' b'12324' b'14480' b'32'
b'1228' b'88' b'3476' b'7984' b'11924' b'3460' b'78436' b'54988' b'55048'
b'1212' b'7816' b'108104' b'10896' b'2848' b'41864' b'77996' b'5388'
b'7136' b'65172' b'124' b'1500' b'9884' b'1560' b'3720' b'315932' b'7152'
b'9188' b'64596' b'8216' b'300016' b'108' b'1276' b'2192' b'2256'
b'56896' b'51504' b'24804' b'16072' b'735820' b'538028' b'2772' b'16624'
b'1596' b'51972' b'4192' b'82864' b'6348' b'19420' b'2496' b'94160'
b'67364' b'29612' b'80024' b'3808' b'9432' b'2908' b'4748' b'19740'
b'11052' b'834652' b'1344' b'583488' b'4948' b'5024' b'66476' b'64636'
b'24816' b'6364' b'74440' b'76364' b'2260' b'40896' b'8172' b'762880'
b'49664' b'1452' b'3236' b'102400' b'1360' b'1528' b'2520' b'2512'
b'2540' b'951380' b'66940' b'9956' b'57616' b'274296' b'5084' b'999344'
b'21776' b'12656' b'11220' b'3092' b'28840' b'160' b'7272' b'6032'
b'16676' b'9200' b'3060' b'14760' b'51788' b'5360' b'2180' b'40620'
b'14292' b'1268' b'12848' b'10448' b'4964' b'42488' b'17452' b'5080'
b'12176' b'139840' b'83872' b'49684' b'41524' b'767708' b'2108' b'15608'
b'8252' b'112988' b'10036' b'50848' b'6224' b'6156' b'7896' b'949800'
b'3124' b'49672' b'139636' b'76404' b'780084' b'5244' b'2584' b'4300'
b'66760' b'57084' b'4656' b'20' b'6356' b'2516' b'2680' b'19088'
b'989268' b'51672' b'5624' b'10028' b'24188' b'3260' b'1240' b'1456'
b'151936' b'51372' b'5872' b'7632' b'2412' b'2556' b'913772' b'454264'
b'813516' b'57540' b'43024' b'2688' b'6784' b'4240' b'3596' b'64056'
b'10428' b'65052' b'5664' b'66348' b'4700' b'3668' b'65836' b'1612'
b'5596' b'6292' b'682264' b'10624' b'2296' b'71500' b'2588' b'4408'
b'1244' b'6568' b'1220' b'949872' b'4704' b'3344' b'3964' b'66460'
b'3744' b'3600' b'455536' b'2568' b'293556' b'2608' b'908208' b'2208'
b'12136' b'42544' b'2144' b'3488' b'67624' b'81296' b'65096' b'5232'
b'10092' b'41544' b'67704' b'7668' b'2860' b'2244' b'64664' b'1336'
b'76708' b'67444' b'3200' b'27300' b'31068' b'8560' b'1304' b'25748'
b'12544' b'10116' b'5332' b'48276' b'33044' b'4648' b'4164' b'27072'
b'4196' b'73540' b'86924' b'28' b'287820' b'4104' b'91936' b'11872'
b'117756' b'11660' b'764728' b'79408' b'2388' b'68484' b'51660' b'1584'
b'1468' b'3144' b'6132' b'4464' b'5704' b'5076' b'56136' b'76' b'7224'
b'3816' b'36444' b'2232' b'51752' b'11836' b'60944' b'100308' b'60856'
b'99648' b'88064' b'65488' b'24912' b'592792' b'1200' b'12636' b'65452'
b'1188' b'48736' b'22320' b'65524' b'5508' b'35772' b'2488' b'191112'
b'2136' b'587804' b'7492' b'234828' b'25116' b'5312' b'561804' b'6452'
b'7436' b'48412' b'5344' b'4988' b'14524' b'2148' b'21336' b'6380'
b'6664' b'55000' b'13824' b'7720' b'68' b'55064' b'5464' b'365544'
b'9628' b'68304' b'8384' b'51292' b'5004' b'16264' b'184120' b'4112'
b'64888' b'67508' b'3228' b'65828' b'561488' b'682952' b'47852' b'66796'
b'60444' b'12204' b'67976' b'41040' b'2504' b'33276' b'75928' b'95320'
b'215712' b'65016' b'234124' b'51564' b'60940' b'16536' b'65932' b'4036'
b'8600' b'344412' b'9780' b'80216' b'65872' b'54520' b'233732' b'8592'
b'8488' b'2416' b'36504' b'84144' b'5228' b'96776' b'65824' b'10808'
b'63996' b'10348' b'21912' b'4480' b'2696' b'24916' b'9320' b'5636'
b'767868' b'12056' b'25772' b'908212' b'400992' b'23988' b'24792' b'4152'
b'7068' b'89756' b'2352' b'16652' b'946776' b'6204' b'2524' b'4328'
b'66700' b'8300' b'2348' b'82836' b'18448' b'465656' b'125324' b'7532'
b'9504' b'3660' b'40584' b'36' b'25784' b'74916' b'49756' b'955768'
b'81828' b'582564' b'1320' b'965600' b'3796' b'12716' b'601476' b'17436'
b'6848' b'22100' b'2140' b'25684' b'10104' b'42876' b'63016' b'34600'
b'55228' b'30816' b'67552' b'44412' b'6212' b'64748' b'5444' b'2152'
b'5960' b'9408' b'14764' b'20812' b'21944' b'59332' b'2864' b'3700'
b'3096' b'1572' b'55332' b'187560' b'51840' b'48408' b'9908' b'12404'
b'7108' b'3880' b'444016' b'49552' b'12360' b'4936' b'55172' b'71128'
b'44588' b'9496' b'8204' b'87196' b'86380' b'2460' b'547432' b'51444'
b'606420' b'1548' b'170900' b'108972' b'78032' b'51756' b'108812'
b'11976' b'4520' b'23408' b'75124' b'528252' b'295104' b'59624' b'12200'
b'9360' b'4172' b'60752' b'1556' b'1552' b'739472' b'6952' b'97408'
b'1224' b'4276' b'71148' b'5184' b'1324' b'4176']
Feature: top_4_7_day_non_fresh_product
Tensor: [b'51444' b'5056' b'2624' b'1500' b'88' b'55128' b'10260' b'79604'
b'65932' b'3200' b'51504' b'6380' b'5344' b'3056' b'64824' b'2412'
b'1356' b'1464' b'66460' b'1424' b'83740' b'65336' b'14760' b'1244'
b'124' b'2280' b'7080' b'300016' b'36584' b'41548' b'28' b'16696' b'NA'
b'36836' b'1452' b'4116' b'99648' b'49676' b'50876' b'12056' b'4408'
b'2416' b'62732' b'2492' b'7876' b'38164' b'5080' b'7108' b'57164'
b'51348' b'173052' b'5636' b'1232' b'1228' b'37752' b'10428' b'2556'
b'5736' b'122680' b'118652' b'97792' b'8388' b'2192' b'142952' b'3464'
b'1312' b'40360' b'65428' b'12668' b'17596' b'7736' b'4816' b'1368'
b'2572' b'11168' b'3660' b'12136' b'40412' b'7224' b'10036' b'10756'
b'682264' b'1304' b'175588' b'28368' b'2672' b'2296' b'57152' b'66564'
b'2332' b'16224' b'24788' b'5920' b'2408' b'1184' b'749048' b'1344'
b'5228' b'67508' b'4696' b'175948' b'2460' b'27208' b'1284' b'2732'
b'24836' b'85296' b'4560' b'34880' b'3124' b'914476' b'4948' b'11884'
b'3120' b'3808' b'23012' b'17756' b'5992' b'9456' b'2132' b'1276' b'1560'
b'771140' b'1240' b'6348' b'8252' b'300012' b'84720' b'38148' b'68372'
b'4196' b'14756' b'2512' b'958324' b'11664' b'97408' b'66108' b'11924'
b'5704' b'2352' b'105744' b'22308' b'3744' b'64828' b'3100' b'11224'
b'1352' b'71188' b'450748' b'90708' b'51372' b'36444' b'9344' b'66492'
b'2704' b'7984' b'6156' b'9924' b'1404' b'14524' b'6452' b'3084' b'3484'
b'55532' b'5624' b'32920' b'37916' b'1324' b'15944' b'21912' b'5688'
b'14480' b'905692' b'721788' b'16536' b'64088' b'2676' b'62252' b'8592'
b'562740' b'24896' b'52536' b'65888' b'7816' b'464592' b'8216' b'5000'
b'3392' b'3276' b'51972' b'583288' b'11944' b'15816' b'2180' b'3144'
b'5576' b'743952' b'68560' b'120872' b'949656' b'14764' b'9628' b'105900'
b'65424' b'48952' b'74916' b'11424' b'7492' b'55348' b'3196' b'2568'
b'35440' b'8920' b'3920' b'2532' b'10412' b'110196' b'84160' b'3596'
b'6072' b'2188' b'106840' b'48408' b'62636' b'8500' b'58920' b'2144'
b'462960' b'2776' b'2456' b'110144' b'15920' b'449588' b'7500' b'4800'
b'6032' b'5084' b'24' b'4520' b'2848' b'1248' b'103984' b'24912' b'6356'
b'8408' b'12096' b'41512' b'24000' b'5116' b'3108' b'12544' b'16456'
b'28356' b'3060' b'565168' b'2868' b'7436' b'1516' b'10808' b'12176'
b'68028' b'116' b'111608' b'3024' b'749300' b'15228' b'38160' b'1280'
b'11976' b'5136' b'108' b'64636' b'9504' b'66204' b'64888' b'53688'
b'50552' b'1027164' b'54044' b'56592' b'3460' b'4364' b'94812' b'196'
b'41496' b'22232' b'1220' b'7572' b'10092' b'4192' b'56468' b'19172'
b'2588' b'3616' b'11848' b'913772' b'22208' b'732280' b'4692' b'4700'
b'5444' b'2908' b'4936' b'95160' b'948188' b'953008' b'10564' b'57116'
b'489216' b'45572' b'2252' b'80880' b'74700' b'28344' b'2440' b'59300'
b'4484' b'36312' b'43528' b'118412' b'7704' b'10104' b'5776' b'68484'
b'2860' b'2584' b'6456' b'45204' b'12704' b'4172' b'912528' b'68'
b'65496' b'603544' b'36848' b'2108' b'955216' b'4488' b'36488' b'82836'
b'84652' b'42168' b'6364' b'76' b'2772' b'4924' b'78208' b'1556' b'45080'
b'1272' b'394232' b'41516' b'48600' b'10596' b'7608' b'12012' b'48704'
b'23988' b'25684' b'11200' b'66448' b'22476' b'88224' b'9408' b'64468'
b'175644' b'1212' b'63996' b'31112' b'55904' b'60440' b'25128' b'466728'
b'20264' b'20056' b'70476' b'51292' b'10896' b'54920' b'96672' b'5248'
b'12556' b'65452' b'1320' b'5508' b'730552' b'185024' b'405768' b'12436'
b'250384' b'5960' b'37652' b'9956' b'2508' b'95584' b'2348' b'490528'
b'455204' b'1528' b'114356' b'62776' b'52492' b'51756' b'10384' b'3668'
b'16236' b'81644' b'147844' b'83900' b'57024' b'1460' b'823924' b'7072'
b'6612' b'1396' b'74788' b'196688' b'2260' b'68252' b'57244' b'67688'
b'227660' b'37928' b'7832' b'66452' b'48348' b'64728' b'35956' b'10832'
b'6664' b'488608' b'3468' b'14628' b'67708' b'7084' b'16800' b'41872'
b'3600' b'6984' b'537084' b'40672' b'16144' b'211816' b'42600' b'4380'
b'8384' b'82856' b'5312' b'79356' b'54988' b'3488' b'2488' b'2552'
b'501788' b'91280' b'754876' b'962076' b'82320' b'9140' b'11488' b'10868'
b'6120' b'40844' b'164' b'40248' b'75820' b'5004' b'597460' b'2520'
b'9768' b'965604' b'9024' b'12204' b'12264' b'4356' b'63016' b'601492'
b'21672' b'84336' b'56848' b'73408' b'32904' b'42884' b'76768' b'57556'
b'48304' b'71004' b'8176' b'2256' b'22100' b'6288' b'6276' b'21276'
b'5244' b'6792' b'106456' b'6104' b'80780' b'2148' b'31068' b'4760'
b'10792' b'15652' b'3096' b'25748' b'17716' b'2112' b'187684' b'81296'
b'77960' b'10480' b'67412' b'12412' b'112040' b'2872' b'32956' b'48852'
b'71160' b'25696' b'79408' b'47464' b'16668' b'11432' b'12272' b'610392'
b'331880' b'11480' b'908208' b'2484' b'588196' b'8756' b'97980' b'5464'
b'74416' b'968940' b'454504' b'3612' b'77080' b'226108' b'73340' b'45796'
b'70428' b'115332' b'17424' b'957504' b'448748' b'17452' b'949872'
b'73232' b'10064' b'7412' b'776856' b'9908' b'12456' b'2304' b'68328'
b'2176' b'41524' b'7424' b'2688' b'9224']
Feature: top_5_7_day_non_fresh_product
Tensor: [b'88' b'6212' b'65452' b'2460' b'1356' b'NA' b'14724' b'82084' b'1352'
b'3488' b'944924' b'7272' b'100808' b'12552' b'12176' b'2572' b'2508'
b'3144' b'2188' b'681956' b'2624' b'1516' b'989736' b'1452' b'1220'
b'3596' b'4572' b'9780' b'1424' b'67000' b'86960' b'1312' b'2492'
b'64252' b'54436' b'10208' b'112' b'49680' b'48664' b'571896' b'4948'
b'2192' b'65012' b'105744' b'14760' b'44480' b'41872' b'5992' b'75124'
b'60960' b'65288' b'1284' b'2456' b'64088' b'64100' b'16160' b'10092'
b'300016' b'124' b'2608' b'19664' b'2412' b'4812' b'6348' b'2704'
b'71128' b'445108' b'31112' b'51292' b'7876' b'2772' b'12324' b'4172'
b'36828' b'92352' b'78884' b'43860' b'15608' b'11432' b'66760' b'1328'
b'3964' b'1304' b'173416' b'2688' b'78052' b'9416' b'501788' b'25128'
b'55128' b'52460' b'76' b'6816' b'3868' b'1276' b'2672' b'5232' b'67512'
b'9188' b'64596' b'1252' b'41596' b'2676' b'2540' b'3744' b'3056' b'2280'
b'2132' b'966320' b'8212' b'42484' b'1500' b'16072' b'113580' b'5080'
b'3644' b'242756' b'3736' b'3200' b'9368' b'24584' b'37652' b'41584'
b'6292' b'9472' b'2256' b'3720' b'4268' b'2556' b'6352' b'8444' b'57084'
b'193816' b'14808' b'59580' b'3880' b'37936' b'5736' b'1232' b'3660'
b'3392' b'80460' b'908208' b'146088' b'9504' b'3808' b'108104' b'4040'
b'65060' b'3468' b'51304' b'66476' b'843404' b'60440' b'55396' b'36848'
b'53688' b'74416' b'10276' b'106456' b'11024' b'8048' b'7560' b'10412'
b'3028' b'7140' b'66460' b'24228' b'8500' b'8216' b'71120' b'9956'
b'60456' b'4408' b'9884' b'65096' b'7716' b'3668' b'5988' b'54508'
b'2296' b'28376' b'65248' b'66376' b'363420' b'14732' b'557064' b'65260'
b'68500' b'64824' b'10892' b'48408' b'60856' b'14376' b'5476' b'4192'
b'12516' b'20752' b'3124' b'23176' b'12636' b'6240' b'1416' b'1228'
b'73232' b'40396' b'10756' b'406932' b'70544' b'2524' b'59840' b'173052'
b'9792' b'399060' b'67988' b'10480' b'35448' b'49164' b'3436' b'2872'
b'6612' b'65836' b'27208' b'60388' b'110200' b'761164' b'4196' b'17756'
b'2632' b'176976' b'51568' b'120872' b'9436' b'817352' b'6512' b'8768'
b'3096' b'80508' b'823924' b'3060' b'36764' b'450340' b'18152' b'4540'
b'7548' b'10616' b'3092' b'6568' b'60708' b'5184' b'8408' b'13520'
b'16732' b'1404' b'66040' b'12260' b'4104' b'115616' b'65824' b'25108'
b'86116' b'81328' b'7712' b'3516' b'764028' b'3344' b'8868' b'2440'
b'51504' b'17436' b'3700' b'1280' b'70744' b'537168' b'3564' b'80776'
b'42076' b'35520' b'2512' b'8556' b'4240' b'2588' b'107352' b'71428'
b'183896' b'88168' b'65492' b'54456' b'65828' b'12360' b'4148' b'7736'
b'5056' b'6452' b'15920' b'2516' b'250384' b'2464' b'42488' b'36836'
b'64084' b'11000' b'48412' b'77848' b'10808' b'44492' b'2732' b'57152'
b'64616' b'1560' b'5576' b'6276' b'5700' b'9324' b'4524' b'6188' b'1256'
b'64056' b'3920' b'550912' b'80536' b'70820' b'90404' b'19172' b'78308'
b'7936' b'5308' b'120' b'65428' b'2520' b'7224' b'68512' b'20000'
b'56584' b'45248' b'176164' b'26404' b'11848' b'24940' b'775664' b'5008'
b'213436' b'10420' b'7764' b'28652' b'104' b'4564' b'3476' b'3480'
b'949832' b'4960' b'51444' b'3444' b'454264' b'227660' b'6156' b'64680'
b'147844' b'42176' b'9680' b'60944' b'2776' b'21668' b'105428' b'586936'
b'36312' b'79536' b'49536' b'77892' b'70624' b'2292' b'12056' b'46036'
b'65932' b'205884' b'160' b'5704' b'44412' b'35240' b'84488' b'16696'
b'3032' b'65916' b'24816' b'54524' b'17196' b'10320' b'84016' b'5116'
b'66680' b'601476' b'51676' b'45612' b'66564' b'7424' b'89136' b'821476'
b'26628' b'12780' b'67704' b'60556' b'1324' b'2112' b'303504' b'28608'
b'68316' b'51584' b'7984' b'5228' b'102292' b'5652' b'2304' b'6132'
b'732280' b'99288' b'70368' b'70516' b'28404' b'22204' b'183168' b'8600'
b'91936' b'488608' b'2860' b'4164' b'8452' b'3108' b'10428' b'86348'
b'595828' b'9360' b'3824' b'5332' b'70332' b'78092' b'442208' b'76764'
b'9868' b'188248' b'51752' b'75160' b'76816' b'42336' b'12556' b'16544'
b'2584' b'7744' b'6204' b'33276' b'70784' b'207048' b'65860' b'22288'
b'2956' b'1224' b'40680' b'65804' b'5136' b'28348' b'65432' b'8384'
b'2108' b'4816' b'1512' b'473168' b'14480' b'11560' b'5924' b'55620'
b'956856' b'113544' b'842888' b'125220' b'34600' b'16272' b'56460'
b'1568' b'3276' b'83872' b'7072' b'7584' b'70364' b'15952' b'17452'
b'15960' b'5244' b'68560' b'27212' b'9040' b'40636' b'49448' b'947460'
b'609960' b'2348' b'64000' b'105900' b'64228' b'66108' b'57760' b'70888'
b'6068' b'92472' b'78408' b'7528' b'9756' b'41516' b'10028' b'7108'
b'11160' b'66312' b'136' b'5596' b'8064' b'68460' b'4520' b'76412'
b'62080' b'4700' b'436632' b'97408' b'3976' b'66016' b'614084' b'5032'
b'5508' b'421128' b'108296' b'74700' b'44404' b'551076' b'757168' b'5240'
b'695320' b'28700' b'66304' b'88064' b'1548' b'47592' b'26356' b'36444'
b'22356' b'450748' b'9820' b'11696' b'13884' b'7436' b'737940' b'12088'
b'573900' b'990568' b'9408' b'832488' b'147112' b'94460' b'36568'
b'74640' b'176692' b'43712' b'42392' b'960788' b'113312' b'2260' b'52000'
b'17680' b'32832' b'8896' b'103080' b'74440' b'3460' b'4160' b'12656'
b'103300' b'9600' b'5076' b'192600' b'50644' b'2708' b'105184' b'12676'
b'65688' b'36580' b'5616' b'10684']
Feature: top_1_15_day_non_fresh_product
Tensor: [b'2192' b'124' b'1352' b'1356' b'2456' b'968504' b'82084' b'2188'
b'956856' b'1528' b'736296' b'245488' b'67624' b'51504' b'2132' b'4172'
b'557064' b'88' b'57152' b'9140' b'97792' b'2496' b'64616' b'3600'
b'1312' b'1500' b'66460' b'11424' b'54436' b'10208' b'8500' b'10104'
b'1380' b'1184' b'399060' b'1276' b'4648' b'16696' b'17756' b'5344'
b'1560' b'1232' b'14764' b'2492' b'108104' b'45244' b'2572' b'771148'
b'1228' b'2412' b'60928' b'24' b'28' b'813520' b'1516' b'8592' b'2140'
b'12672' b'49552' b'44796' b'2732' b'31068' b'565672' b'1424' b'1324'
b'1452' b'4408' b'11664' b'54316' b'41548' b'809012' b'14828' b'7108'
b'64088' b'20' b'455204' b'52652' b'48688' b'7736' b'53648' b'2520'
b'3200' b'64252' b'66476' b'8388' b'53032' b'6364' b'7152' b'44480'
b'36328' b'8172' b'6348' b'25128' b'300016' b'92344' b'1284' b'8104'
b'65932' b'6564' b'25108' b'70516' b'1328' b'7668' b'764100' b'7224'
b'150304' b'6032' b'116452' b'4328' b'3808' b'1272' b'2352' b'24816'
b'3276' b'277808' b'1304' b'10808' b'12136' b'22320' b'83872' b'326180'
b'14628' b'193816' b'959932' b'3532' b'9956' b'2704' b'8252' b'65904'
b'278172' b'583488' b'2256' b'370204' b'4464' b'3976' b'3488' b'743952'
b'61112' b'10756' b'209364' b'74916' b'55348' b'3196' b'51292' b'3880'
b'4816' b'56' b'1332' b'818192' b'2260' b'4948' b'2568' b'2584' b'108'
b'4564' b'6568' b'84160' b'5348' b'12552' b'3668' b'490528' b'5672'
b'70292' b'65812' b'3092' b'84652' b'2416' b'3344' b'51448' b'41872'
b'1216' b'455536' b'447028' b'488608' b'84372' b'3236' b'5240' b'3744'
b'65428' b'66564' b'173052' b'9416' b'13680' b'147824' b'36504' b'502228'
b'2556' b'65096' b'293556' b'160' b'11632' b'33180' b'49756' b'90296'
b'3460' b'4780' b'41864' b'785820' b'22160' b'5576' b'5444' b'96600'
b'27072' b'3144' b'1512' b'216276' b'26404' b'597840' b'80536' b'2676'
b'6612' b'36568' b'95460' b'68196' b'219788' b'7980' b'5076' b'982088'
b'64228' b'37684' b'25132' b'2148' b'12556' b'16600' b'11236' b'2860'
b'164' b'3920' b'989708' b'48736' b'577000' b'77380' b'9544' b'957296'
b'11732' b'539436' b'557748' b'908208' b'77908' b'4160' b'5080' b'168'
b'581988' b'592792' b'103520' b'8384' b'63120' b'84' b'10896' b'89136'
b'72' b'24980' b'18196' b'4812' b'3056' b'60944' b'2508' b'9884' b'3484'
b'83740' b'3720' b'501784' b'28200' b'776584' b'1240' b'14480' b'1404'
b'4192' b'6212' b'2280' b'64824' b'12204' b'24836' b'1456' b'1360'
b'1548' b'9344' b'113520' b'12532' b'10428' b'65828' b'12176' b'54976'
b'11944' b'2112' b'6664' b'6156' b'68304' b'142452' b'5636' b'57148'
b'1224' b'3564' b'65916' b'5136' b'5688' b'42600' b'5616' b'2208'
b'51556' b'12544' b'114072' b'16748' b'17528' b'12184' b'66796' b'11560'
b'119164' b'91696' b'6104' b'51716' b'11488' b'597344' b'147572' b'12436'
b'1252' b'3124' b'1368' b'52220' b'7584' b'1268' b'7492' b'95916'
b'92164' b'32' b'65012' b'42912' b'73408' b'17596' b'76' b'77936'
b'18948' b'68' b'44412' b'989268' b'9756' b'4104' b'373576' b'121212'
b'753992' b'99648' b'1212' b'455356' b'44860' b'9012' b'7712' b'2268'
b'379780' b'3028' b'5404' b'2304' b'24112' b'5736' b'11848' b'65696'
b'97332' b'7896' b'444016' b'10424' b'12360' b'33276' b'6452' b'414468'
b'71128' b'87020' b'2296' b'54988' b'454264' b'6072' b'9040' b'573900'
b'1460' b'96944' b'2288' b'2264' b'83596' b'64112' b'60904' b'4520'
b'6524' b'45796' b'3516' b'448748' b'782776' b'55156' b'37752' b'49448'
b'8808' b'819108' b'809428' b'4560' b'6224' b'5236' b'48408' b'3736'
b'65288' b'306644' b'10320' b'2532' b'7424' b'2244']
Feature: top_2_15_day_non_fresh_product
Tensor: [b'51504' b'51716' b'65436' b'1352' b'1228' b'2492' b'1356' b'41552'
b'5844' b'1764' b'4240' b'10104' b'1312' b'609960' b'124' b'66564'
b'70516' b'2188' b'66460' b'7668' b'62080' b'1424' b'88' b'8500' b'1284'
b'1332' b'835436' b'60904' b'81096' b'1500' b'12556' b'80460' b'4816'
b'2572' b'12176' b'8960' b'6348' b'25108' b'2416' b'3668' b'41872'
b'14756' b'37752' b'28' b'4196' b'300012' b'1560' b'3460' b'97792'
b'173436' b'36824' b'1184' b'142952' b'5988' b'40456' b'1276' b'74704'
b'31112' b'60856' b'65828' b'2860' b'2584' b'3720' b'66088' b'2280'
b'5240' b'64596' b'10756' b'300016' b'12204' b'482992' b'3200' b'2296'
b'44872' b'503952' b'24804' b'565592' b'65688' b'3344' b'78672' b'16624'
b'2676' b'7152' b'70784' b'2456' b'82864' b'19664' b'2132' b'1620'
b'65052' b'3092' b'2192' b'4748' b'6612' b'118652' b'210984' b'5992'
b'67412' b'57152' b'32' b'3056' b'2352' b'1240' b'4692' b'66476' b'16600'
b'1304' b'917780' b'40896' b'75152' b'6452' b'2516' b'2440' b'102400'
b'24' b'4656' b'2412' b'3744' b'51672' b'8560' b'70340' b'7108' b'965524'
b'31068' b'10792' b'36568' b'989584' b'10276' b'453164' b'8592' b'414180'
b'3124' b'16676' b'4520' b'10036' b'70444' b'9040' b'135372' b'50876'
b'1324' b'20' b'4524' b'49712' b'160' b'5688' b'50552' b'45572' b'583488'
b'2704' b'455536' b'4408' b'49732' b'348700' b'6356' b'2732' b'2176'
b'4480' b'71452' b'139872' b'14764' b'4276' b'5616' b'5344' b'68560'
b'2488' b'6364' b'1344' b'766544' b'64088' b'173052' b'1368' b'399060'
b'455204' b'68512' b'3920' b'9036' b'105632' b'1452' b'NA' b'50636'
b'2256' b'454264' b'36444' b'73076' b'54988' b'4172' b'67028' b'82364'
b'76200' b'2776' b'907328' b'48664' b'2608' b'449588' b'5664' b'65428'
b'5388' b'6352' b'65836' b'1468' b'5596' b'89688' b'51444' b'24912'
b'81596' b'11336' b'2140' b'957420' b'8624' b'3196' b'4704' b'73268'
b'227156' b'1232' b'8556' b'90236' b'1536' b'1456' b'66796' b'68028'
b'4924' b'2688' b'3564' b'2144' b'115876' b'65932' b'87612' b'1420'
b'17756' b'36852' b'12552' b'65708' b'73244' b'5736' b'2508' b'33412'
b'49760' b'5432' b'11924' b'1220' b'5080' b'2552' b'565772' b'10808'
b'6132' b'2540' b'59620' b'33044' b'3276' b'37552' b'18448' b'43932'
b'54044' b'67552' b'766024' b'11512' b'153472' b'5960' b'6568' b'8064'
b'3096' b'475956' b'2304' b'65816' b'66568' b'44480' b'5704' b'74684'
b'61064' b'2848' b'6052' b'51292' b'3488' b'24940' b'71428' b'2672'
b'2388' b'11344' b'123432' b'27256' b'1584' b'4564' b'816152' b'2240'
b'7224' b'12848' b'592792' b'9188' b'450356' b'1244' b'8388' b'12360'
b'1360' b'7716' b'10892' b'10200' b'45080' b'82604' b'83740' b'444016'
b'561804' b'7492' b'57148' b'4040' b'48704' b'184120' b'28376' b'14524'
b'1436' b'16696' b'3468' b'3784' b'5508' b'8424' b'365544' b'8384'
b'103884' b'2556' b'251808' b'58312' b'36848' b'24984' b'97376' b'9956'
b'35988' b'41040' b'10428' b'67976' b'5228' b'2512' b'2504' b'33276'
b'7984' b'145892' b'64824' b'63996' b'6512' b'776524' b'51756' b'15920'
b'16536' b'3596' b'530892' b'2484' b'10384' b'43976' b'71004' b'80656'
b'6748' b'3060' b'64096' b'48600' b'233732' b'25116' b'108' b'3144'
b'19740' b'765808' b'82836' b'89560' b'62252' b'251884' b'2168' b'10224'
b'16236' b'5444' b'12544' b'7080' b'7856' b'10184' b'970944' b'7744'
b'2696' b'11468' b'24916' b'51372' b'83132' b'3868' b'6380' b'6100'
b'1256' b'964104' b'65424' b'1516' b'17104' b'68460' b'66348' b'3028'
b'3324' b'1552' b'1280' b'17596' b'214408' b'5672' b'5232' b'8644'
b'182520' b'85456' b'1320' b'65968' b'903488' b'11848' b'601476' b'3284'
b'3736' b'70292' b'66620' b'9276' b'746308' b'55440' b'965600' b'4380'
b'59580' b'48264' b'1404' b'375392' b'60444' b'6968' b'5576' b'7228'
b'67244' b'214472' b'286488' b'84356' b'4948' b'17468' b'1272' b'13636'
b'11424' b'10028' b'613884' b'612080' b'144788' b'59332' b'9224'
b'193264' b'11432' b'64664' b'9408' b'24788' b'24116' b'6032' b'1572'
b'5676' b'203036' b'74916' b'6816' b'24640' b'16' b'724144' b'8124'
b'3956' b'65812' b'8920' b'200112' b'273364' b'51284' b'749048' b'4160'
b'24932' b'170900' b'737736' b'9132' b'6276' b'683160' b'48556' b'957816'
b'502356' b'24836' b'4956' b'65904' b'31116' b'2260' b'49664' b'234984'
b'11052' b'20984' b'913732' b'12436' b'1224' b'557344' b'91612' b'4812'
b'4016' b'107352' b'14584' b'2112']
Feature: top_3_15_day_non_fresh_product
Tensor: [b'88' b'1284' b'65452' b'119632' b'118652' b'2624' b'1352' b'76' b'2572'
b'7424' b'3488' b'66088' b'70312' b'51580' b'1312' b'2508' b'65932'
b'1228' b'94472' b'65444' b'59608' b'66564' b'65336' b'14760' b'1244'
b'5616' b'2732' b'1620' b'66108' b'1424' b'108948' b'1356' b'1232'
b'2488' b'949352' b'2556' b'11200' b'8500' b'54524' b'455536' b'2132'
b'19740' b'62732' b'1368' b'5316' b'14764' b'2704' b'173052' b'32'
b'9376' b'6452' b'590676' b'5312' b'65428' b'4564' b'78436' b'55048'
b'4948' b'3108' b'114296' b'9344' b'6348' b'41864' b'77996' b'24816'
b'1512' b'6844' b'1184' b'3200' b'124' b'1500' b'11168' b'1560' b'1404'
b'19068' b'3056' b'67216' b'5080' b'11432' b'293556' b'16132' b'86780'
b'5812' b'2688' b'56896' b'16800' b'NA' b'6816' b'538028' b'2772' b'3736'
b'61220' b'51784' b'27296' b'331880' b'1240' b'64596' b'7224' b'2492'
b'3744' b'51504' b'1220' b'1452' b'300016' b'80024' b'550912' b'9432'
b'36584' b'82836' b'7720' b'1344' b'583488' b'24' b'4696' b'300012'
b'64636' b'57084' b'60856' b'41872' b'56592' b'59580' b'25136' b'74700'
b'57152' b'42424' b'2348' b'1276' b'94160' b'146088' b'749928' b'10296'
b'2520' b'56472' b'1304' b'11224' b'24836' b'2140' b'3144' b'68496'
b'3392' b'51444' b'73276' b'12336' b'2148' b'160' b'548824' b'171184'
b'10412' b'7108' b'399060' b'5100' b'28' b'596104' b'40456' b'1332'
b'2192' b'81096' b'32920' b'57116' b'4964' b'10036' b'9036' b'68484'
b'613884' b'49684' b'25132' b'8644' b'51372' b'4520' b'14732' b'112988'
b'3336' b'14524' b'2332' b'2208' b'2172' b'7896' b'78876' b'4408' b'3124'
b'5184' b'76404' b'5244' b'2584' b'7492' b'64680' b'31068' b'909888'
b'2456' b'4572' b'989268' b'5624' b'964960' b'45440' b'76808' b'65892'
b'151936' b'20' b'5872' b'786164' b'761164' b'4816' b'454264' b'4000'
b'43024' b'9436' b'3564' b'2392' b'3084' b'68304' b'2412' b'52004'
b'7712' b'10428' b'450340' b'7500' b'65436' b'6568' b'7608' b'5084'
b'12324' b'1612' b'5596' b'682264' b'82464' b'82244' b'11800' b'2588'
b'70516' b'83740' b'15840' b'4704' b'948284' b'97792' b'2676' b'8868'
b'12656' b'2568' b'328828' b'66460' b'75124' b'25772' b'9544' b'2608'
b'7736' b'908208' b'1248' b'3476' b'11768' b'4700' b'32856' b'76588'
b'104868' b'5028' b'10092' b'2516' b'7668' b'2244' b'64664' b'2672'
b'54988' b'10992' b'67444' b'3480' b'10064' b'4016' b'6632' b'4196'
b'80460' b'64952' b'4148' b'2872' b'488872' b'8216' b'5344' b'557064'
b'16668' b'6156' b'64088' b'16536' b'45612' b'4936' b'66448' b'91936'
b'11872' b'8552' b'3060' b'764728' b'2388' b'1468' b'120' b'59300'
b'43528' b'908244' b'12176' b'555808' b'20152' b'34380' b'2352' b'24940'
b'64004' b'557344' b'3596' b'2540' b'3092' b'105900' b'71296' b'10956'
b'24912' b'2108' b'603544' b'12056' b'12636' b'1188' b'2860' b'8252'
b'2256' b'5508' b'16176' b'60944' b'191112' b'2136' b'14532' b'7804'
b'11708' b'84' b'1224' b'586936' b'9956' b'957176' b'5736' b'7436'
b'11664' b'62860' b'21336' b'9408' b'12580' b'949668' b'168' b'5116'
b'22916' b'3920' b'16264' b'4104' b'184120' b'12556' b'64888' b'67508'
b'193816' b'3228' b'42608' b'37916' b'988636' b'47852' b'5388' b'405768'
b'577092' b'5236' b'3600' b'108' b'83872' b'95320' b'71440' b'540040'
b'489120' b'1456' b'66332' b'2848' b'961820' b'4036' b'98160' b'20880'
b'110032' b'11924' b'145892' b'65260' b'57024' b'1212' b'8592' b'48600'
b'4240' b'65836' b'84160' b'17756' b'5704' b'6132' b'36444' b'75696'
b'6628' b'4248' b'51752' b'10808' b'9504' b'67688' b'10348' b'6664'
b'488608' b'5944' b'8384' b'10892' b'33276' b'5636' b'908212' b'400992'
b'7632' b'2912' b'52000' b'51716' b'2296' b'7068' b'180204' b'12544'
b'916296' b'16652' b'108712' b'7880' b'5476' b'14480' b'205884' b'25696'
b'43108' b'8300' b'7168' b'9320' b'48608' b'54976' b'43384' b'770864'
b'4328' b'24784' b'82364' b'962268' b'51448' b'36' b'25784' b'49756'
b'83460' b'66960' b'2524' b'74436' b'4608' b'1320' b'2152' b'65664'
b'965604' b'3460' b'3700' b'3260' b'56848' b'496468' b'36912' b'4172'
b'52672' b'42876' b'121728' b'40864' b'71004' b'3668' b'25108' b'551076'
b'25128' b'777220' b'5444' b'2212' b'1384' b'64748' b'54980' b'54436'
b'6792' b'750428' b'2180' b'5984' b'36328' b'9788' b'15652' b'4192'
b'8080' b'3096' b'1420' b'55332' b'64100' b'443368' b'51840' b'48408'
b'66304' b'26544' b'12404' b'5240' b'35584' b'44588' b'36824' b'8204'
b'16600' b'5576' b'966360' b'2460' b'113336' b'828748' b'51348' b'9188'
b'2696' b'108972' b'53684' b'46988' b'413740' b'679560' b'90296' b'49712'
b'5004' b'528252' b'9360' b'42612' b'111148' b'739472' b'97408' b'12456'
b'183240' b'45712' b'51868' b'1328' b'1324' b'2232' b'4176']
Feature: top_4_15_day_non_fresh_product
Tensor: [b'1284' b'2572' b'2624' b'124' b'1356' b'1424' b'31068' b'1232' b'65932'
b'64888' b'3484' b'5736' b'5344' b'65060' b'64824' b'2412' b'88' b'1452'
b'537340' b'66476' b'17756' b'1220' b'211816' b'12556' b'40456' b'2192'
b'300016' b'66460' b'3460' b'4816' b'2492' b'64748' b'5464' b'1560'
b'565660' b'2608' b'99648' b'54980' b'50876' b'1276' b'65140' b'68484'
b'7108' b'38164' b'5992' b'3428' b'12324' b'1352' b'3344' b'61448'
b'1228' b'7984' b'11924' b'122680' b'24' b'63120' b'3744' b'24816'
b'10896' b'79752' b'1312' b'2860' b'65436' b'2144' b'4148' b'7136'
b'65172' b'108104' b'9884' b'40412' b'140912' b'3880' b'7152' b'48736'
b'4708' b'211568' b'453788' b'9040' b'16600' b'57152' b'98044' b'14732'
b'1240' b'19740' b'NA' b'13860' b'2408' b'4948' b'66348' b'70888'
b'64088' b'6276' b'1332' b'34664' b'53032' b'19420' b'2496' b'3056'
b'12176' b'4284' b'450748' b'4648' b'3124' b'914476' b'11804' b'9672'
b'7528' b'10428' b'313612' b'9308' b'7336' b'834652' b'9456' b'2256'
b'14584' b'771140' b'4748' b'1436' b'74440' b'544116' b'1256' b'66088'
b'76364' b'2260' b'4464' b'62080' b'3092' b'5136' b'100292' b'2416'
b'1360' b'11468' b'12204' b'22308' b'2180' b'2332' b'7424' b'4172'
b'3824' b'170036' b'57616' b'274296' b'5084' b'1500' b'12656' b'2552'
b'28840' b'2132' b'4104' b'56' b'14760' b'51788' b'2140' b'2540'
b'915816' b'40620' b'356280' b'573728' b'65096' b'84720' b'1456' b'5080'
b'9376' b'16236' b'71428' b'34640' b'28' b'1324' b'12544' b'41524'
b'8680' b'63016' b'15608' b'562740' b'5132' b'52536' b'10104' b'3200'
b'464592' b'60856' b'1556' b'496796' b'3276' b'8388' b'756752' b'36444'
b'6348' b'3144' b'108' b'66760' b'64636' b'2504' b'10488' b'4656'
b'908208' b'3596' b'2516' b'19172' b'9384' b'41552' b'11424' b'7492'
b'8500' b'8880' b'588416' b'3468' b'93884' b'3392' b'9956' b'331880'
b'51504' b'2352' b'65016' b'5116' b'56460' b'48408' b'2704' b'10724'
b'1184' b'4240' b'3644' b'1212' b'342536' b'52316' b'7980' b'12096'
b'3060' b'2508' b'9140' b'966360' b'20' b'2848' b'45124' b'1572' b'6292'
b'10624' b'150100' b'6568' b'38452' b'5228' b'4936' b'1244' b'42084'
b'2772' b'4564' b'3964' b'7632' b'2868' b'5232' b'8868' b'1516' b'293556'
b'71484' b'4192' b'8552' b'436864' b'66564' b'4408' b'43196' b'11976'
b'5616' b'51292' b'3488' b'9504' b'7224' b'53688' b'11024' b'14480'
b'4364' b'2676' b'61112' b'557344' b'56076' b'955216' b'11220' b'7572'
b'5248' b'610036' b'10792' b'6820' b'764628' b'68204' b'26508' b'90340'
b'2732' b'5332' b'965524' b'24980' b'5636' b'51716' b'16544' b'765204'
b'2908' b'7176' b'24836' b'73540' b'86924' b'2348' b'64056' b'2212'
b'57116' b'160' b'21152' b'70820' b'74700' b'51660' b'1584' b'5688'
b'14828' b'68512' b'4380' b'823960' b'44416' b'51484' b'51304' b'3236'
b'11664' b'2588' b'172' b'51372' b'4036' b'100308' b'6356' b'10036'
b'3480' b'20180' b'88064' b'44992' b'65428' b'2208' b'57644' b'2512'
b'16128' b'25132' b'2108' b'3028' b'6156' b'16696' b'82836' b'65012'
b'762820' b'2188' b'4924' b'41512' b'51424' b'14500' b'587804' b'244148'
b'5312' b'6212' b'8592' b'6452' b'12012' b'749048' b'4988' b'2900'
b'8384' b'22476' b'9872' b'48900' b'6664' b'17436' b'955756' b'8920'
b'2568' b'7668' b'79408' b'28888' b'2440' b'8768' b'54232' b'4196'
b'8300' b'908236' b'8644' b'54920' b'3600' b'16456' b'65452' b'92284'
b'2456' b'5508' b'561488' b'44884' b'185024' b'65836' b'12532' b'956556'
b'66956' b'6132' b'41040' b'51584' b'66952' b'75928' b'95584' b'215712'
b'759628' b'581988' b'4520' b'79924' b'136' b'11200' b'87020' b'1404'
b'65664' b'169984' b'16168' b'488608' b'76144' b'36568' b'80948'
b'121688' b'25128' b'47936' b'7072' b'551012' b'74788' b'1464' b'56624'
b'2384' b'38056' b'8348' b'4508' b'514072' b'25772' b'32' b'10832'
b'1328' b'682264' b'786928' b'49180' b'67708' b'7084' b'6668' b'16800'
b'777268' b'537084' b'23988' b'12456' b'11428' b'55904' b'13632' b'16676'
b'50668' b'408220' b'24284' b'329244' b'6204' b'2524' b'57148' b'3524'
b'26808' b'5704' b'48424' b'54988' b'12112' b'42460' b'33276' b'11688'
b'45204' b'48624' b'78848' b'60944' b'65972' b'3660' b'577232' b'2460'
b'83740' b'5592' b'70516' b'164' b'3808' b'5056' b'86140' b'66992'
b'4700' b'81828' b'60436' b'2872' b'10808' b'3668' b'12264' b'2296'
b'22100' b'4276' b'79384' b'42884' b'946780' b'24788' b'12380' b'73248'
b'22532' b'31060' b'15920' b'756964' b'430392' b'7532' b'3108' b'2248'
b'5944' b'10224' b'2148' b'4760' b'21944' b'809012' b'33920' b'16136'
b'40360' b'84928' b'82364' b'81296' b'77960' b'234900' b'51448' b'67412'
b'12412' b'48852' b'70648' b'4880' b'9368' b'47464' b'12272' b'87196'
b'65812' b'731448' b'65488' b'86380' b'7608' b'2484' b'537452' b'97980'
b'606420' b'454504' b'78032' b'21704' b'108812' b'68' b'91936' b'149236'
b'5008' b'957504' b'17452' b'66684' b'4328' b'68500' b'32836' b'7412'
b'4696' b'60752' b'1552' b'776856' b'1344' b'485780' b'6364' b'49452'
b'363512' b'2176' b'71148' b'49708' b'9224']
Feature: top_5_15_day_non_fresh_product
Tensor: [b'2732' b'5056' b'1424' b'1500' b'124' b'63116' b'2456' b'1352' b'2192'
b'5316' b'6380' b'1368' b'12552' b'2332' b'88' b'22260' b'8500' b'60940'
b'1600' b'1212' b'36488' b'1452' b'2144' b'965524' b'3596' b'19124'
b'2488' b'300012' b'6632' b'5344' b'2624' b'2516' b'25128' b'2412'
b'565772' b'66564' b'4116' b'16536' b'544116' b'23992' b'2304' b'2492'
b'34580' b'99648' b'41872' b'1284' b'75124' b'30292' b'51348' b'7668'
b'5636' b'1276' b'66760' b'544756' b'1332' b'2508' b'76220' b'3824'
b'83740' b'18468' b'2584' b'2704' b'40360' b'445108' b'5616' b'5388'
b'7736' b'4816' b'2772' b'9792' b'8960' b'3084' b'149492' b'7224'
b'10036' b'1360' b'682264' b'530580' b'1232' b'2388' b'16524' b'50876'
b'78052' b'9416' b'36764' b'16072' b'1404' b'NA' b'21108' b'116796'
b'4908' b'749048' b'28' b'96324' b'1588' b'998260' b'1620' b'12136'
b'175948' b'81596' b'6348' b'79408' b'2296' b'62448' b'24836' b'94160'
b'4564' b'2132' b'3056' b'7744' b'1612' b'11884' b'86400' b'2676'
b'106456' b'3276' b'10092' b'121208' b'949352' b'9472' b'3096' b'51292'
b'4268' b'559092' b'7228' b'108' b'5024' b'1560' b'1280' b'24816'
b'64596' b'2776' b'3124' b'2512' b'4328' b'8808' b'83900' b'5812'
b'829024' b'3236' b'54436' b'16160' b'5704' b'51304' b'76816' b'2540'
b'153888' b'64228' b'6156' b'65932' b'4196' b'71188' b'450748' b'9544'
b'999344' b'2800' b'989708' b'211816' b'7716' b'3092' b'66492' b'1356'
b'4220' b'2696' b'5084' b'68' b'3532' b'3744' b'49532' b'24584' b'64748'
b'55532' b'2568' b'20' b'4408' b'46264' b'25108' b'61696' b'15980'
b'17452' b'809012' b'497400' b'51504' b'1220' b'64088' b'16680' b'68196'
b'62252' b'3668' b'65260' b'49760' b'2860' b'4160' b'1312' b'949800'
b'8216' b'3488' b'3392' b'3460' b'28840' b'1324' b'12516' b'11696'
b'7984' b'11756' b'3772' b'562128' b'3060' b'120872' b'27208' b'3100'
b'4192' b'8212' b'70544' b'11444' b'56624' b'3144' b'1184' b'348820'
b'8920' b'17896' b'5080' b'24' b'10412' b'36504' b'110196' b'7108'
b'15960' b'25124' b'5292' b'813516' b'8388' b'51568' b'12324' b'2572'
b'1556' b'6784' b'2688' b'19740' b'2108' b'595828' b'1188' b'49676'
b'15920' b'1464' b'4364' b'65436' b'10792' b'4700' b'92264' b'3808'
b'51444' b'1248' b'65628' b'2352' b'35548' b'2140' b'3332' b'12540'
b'58356' b'13520' b'6356' b'66040' b'947664' b'203448' b'65252' b'10896'
b'949872' b'24000' b'3108' b'16456' b'12176' b'565168' b'3344' b'331880'
b'45440' b'2440' b'3600' b'360296' b'3700' b'116' b'51752' b'5100'
b'34608' b'749300' b'32' b'2256' b'64004' b'4104' b'25696' b'8556'
b'597420' b'17756' b'3268' b'12272' b'2588' b'58892' b'2496' b'14188'
b'10048' b'193816' b'3720' b'68560' b'71340' b'94812' b'71500' b'18504'
b'22232' b'9884' b'8560' b'1272' b'25748' b'12544' b'780480' b'104'
b'583820' b'11848' b'1516' b'48276' b'2460' b'3200' b'184120' b'2848'
b'1320' b'2244' b'4524' b'41524' b'95160' b'65012' b'344412' b'6968'
b'3880' b'74704' b'25692' b'10428' b'19172' b'78308' b'55064' b'6212'
b'1420' b'34716' b'2520' b'4520' b'84160' b'4484' b'5136' b'20236' b'76'
b'4812' b'14760' b'64136' b'65836' b'32936' b'64096' b'2112' b'11836'
b'60944' b'10072' b'110704' b'113896' b'14524' b'4172' b'14564' b'3480'
b'22160' b'56804' b'51864' b'66476' b'3444' b'36836' b'65452' b'22320'
b'277808' b'36824' b'250336' b'357996' b'777220' b'60928' b'6276' b'1456'
b'56584' b'119344' b'45708' b'1240' b'788508' b'48600' b'219652' b'7084'
b'70396' b'7608' b'46036' b'5736' b'23988' b'3736' b'784484' b'9200'
b'6568' b'11656' b'44412' b'64056' b'64468' b'55000' b'13824' b'7720'
b'12444' b'42604' b'51372' b'9956' b'7980' b'64828' b'5596' b'8408'
b'60440' b'58884' b'4292' b'96672' b'51448' b'65948' b'65892' b'12832'
b'730552' b'52004' b'6132' b'300016' b'56924' b'970360' b'67976' b'16224'
b'45204' b'66348' b'7896' b'2164' b'1416' b'102292' b'464592' b'761392'
b'51716' b'785236' b'1528' b'6664' b'52492' b'2348' b'5988' b'757252'
b'82348' b'1304' b'80216' b'1396' b'7892' b'10892' b'784884' b'1460'
b'823924' b'8452' b'2868' b'2876' b'86348' b'15808' b'36568' b'5332'
b'67196' b'4784' b'8956' b'533500' b'63996' b'4964' b'12556' b'16544'
b'65916' b'76868' b'32960' b'7584' b'25772' b'2148' b'6984' b'7424'
b'12460' b'55908' b'989584' b'16144' b'10196' b'501784' b'3868' b'4380'
b'8384' b'946776' b'47016' b'476188' b'908208' b'4704' b'66452' b'5924'
b'66700' b'65688' b'2552' b'82836' b'582000' b'14608' b'293556' b'125324'
b'63380' b'7492' b'1328' b'3784' b'99288' b'40584' b'5992' b'6452'
b'75820' b'955768' b'597460' b'538028' b'3464' b'965648' b'54360' b'5244'
b'4240' b'8080' b'25684' b'86444' b'947460' b'12656' b'488608' b'64000'
b'76768' b'66108' b'42636' b'93044' b'30816' b'35028' b'1228' b'87016'
b'777928' b'12636' b'11160' b'9408' b'4480' b'42484' b'14764' b'5676'
b'19932' b'8064' b'9140' b'68460' b'56592' b'40396' b'98136' b'2864'
b'17716' b'66016' b'187560' b'421128' b'108296' b'185700' b'74700'
b'44404' b'60856' b'2292' b'49552' b'11000' b'1548' b'47592' b'11664'
b'7704' b'22356' b'76920' b'10208' b'500080' b'13884' b'7436' b'14724'
b'582564' b'5464' b'172188' b'226108' b'51580' b'144' b'5132' b'2248'
b'11560' b'5872' b'42392' b'295104' b'3544' b'67244' b'54324' b'56600'
b'8896' b'65488' b'557344' b'2188' b'9908' b'40' b'31068' b'11592'
b'147232' b'913992' b'2708' b'10480' b'6612' b'184704' b'10684']
Feature: top_1_60_day_non_fresh_product
Tensor: [b'24980' b'124' b'1352' b'1356' b'2456' b'82084' b'65932' b'956856'
b'53676' b'10104' b'245488' b'2572' b'2192' b'51504' b'2188' b'3440'
b'66460' b'88' b'65336' b'8500' b'1284' b'392420' b'2496' b'64616'
b'835436' b'1312' b'1500' b'54436' b'4116' b'40908' b'1424' b'14796'
b'1184' b'399060' b'99648' b'2704' b'17756' b'5344' b'1560' b'4196'
b'65428' b'2492' b'91936' b'7488' b'45244' b'40456' b'77996' b'771148'
b'1228' b'2412' b'24' b'2584' b'40412' b'813520' b'5080' b'66760' b'8592'
b'2140' b'60396' b'64088' b'12552' b'2732' b'31068' b'28' b'3488' b'1452'
b'4408' b'1276' b'25128' b'755440' b'7500' b'66320' b'14828' b'7108'
b'66376' b'57152' b'20' b'455204' b'9904' b'48688' b'7736' b'64252'
b'66476' b'8388' b'1232' b'8444' b'74700' b'36328' b'8172' b'6348'
b'92344' b'7224' b'782860' b'70516' b'274296' b'19740' b'10276' b'602856'
b'234900' b'116452' b'73268' b'3808' b'1272' b'10756' b'3276' b'76'
b'10808' b'6844' b'613884' b'326180' b'193816' b'51372' b'9956' b'8252'
b'65904' b'10792' b'14764' b'11160' b'61112' b'65424' b'9384' b'12176'
b'173052' b'2132' b'3196' b'51292' b'682264' b'750000' b'4816' b'36504'
b'1332' b'538028' b'7632' b'454264' b'64280' b'4564' b'2688' b'2256'
b'2776' b'45568' b'7608' b'3668' b'490528' b'51444' b'82464' b'3200'
b'70292' b'66252' b'84652' b'2416' b'51448' b'5704' b'488608' b'5240'
b'66564' b'3476' b'9416' b'87612' b'147824' b'82996' b'502228' b'2556'
b'10896' b'73244' b'2516' b'14480' b'293556' b'2860' b'64664' b'36568'
b'955216' b'90296' b'83900' b'25108' b'4016' b'60856' b'908212' b'59608'
b'746452' b'2460' b'5444' b'3744' b'96600' b'7176' b'14628' b'216276'
b'65244' b'84372' b'87016' b'4704' b'6612' b'95460' b'68196' b'20000'
b'5076' b'25132' b'12556' b'24940' b'105900' b'949656' b'750380'
b'603544' b'48704' b'78672' b'146784' b'65836' b'11732' b'539436' b'1240'
b'913728' b'25116' b'908208' b'41872' b'77908' b'4160' b'48736' b'300016'
b'28376' b'581988' b'14524' b'64056' b'48900' b'16696' b'8384' b'68392'
b'13824' b'2568' b'365544' b'111260' b'89136' b'2452' b'25136' b'52004'
b'588196' b'2508' b'16116' b'9884' b'3484' b'83740' b'3720' b'501784'
b'64824' b'76588' b'215712' b'540040' b'2540' b'4192' b'6212' b'91720'
b'110032' b'11924' b'404856' b'1456' b'1360' b'16456' b'60940' b'233732'
b'71428' b'11944' b'442208' b'2112' b'6664' b'6156' b'142452' b'66148'
b'3144' b'7984' b'144768' b'24836' b'598956' b'567196' b'32' b'5688'
b'959932' b'5616' b'51556' b'12544' b'10036' b'16748' b'66348' b'3028'
b'12184' b'66796' b'11560' b'17596' b'91696' b'2144' b'3344' b'51716'
b'597344' b'955168' b'6568' b'11848' b'538184' b'49756' b'62916' b'4700'
b'1304' b'746308' b'65664' b'78052' b'7492' b'92164' b'966464' b'60444'
b'12380' b'108' b'67244' b'9544' b'77936' b'18948' b'4948' b'2208'
b'76396' b'5324' b'85348' b'24816' b'12056' b'10028' b'586936' b'121212'
b'7712' b'2248' b'574696' b'5404' b'7572' b'982568' b'65696' b'444016'
b'16' b'761164' b'33276' b'6452' b'2280' b'6364' b'65812' b'56024'
b'65488' b'583488' b'6072' b'12540' b'9040' b'96944' b'1380' b'2264'
b'83596' b'64112' b'60904' b'7152' b'3516' b'3828' b'448748' b'782776'
b'55156' b'75788' b'49448' b'4172' b'331880' b'4560' b'97408' b'183240'
b'3736' b'65288' b'306644' b'55240' b'98496']
Feature: top_2_60_day_non_fresh_product
Tensor: [b'24984' b'2572' b'65436' b'1352' b'1228' b'2492' b'508720' b'48736'
b'2188' b'64632' b'2144' b'59624' b'1500' b'5344' b'1312' b'64824'
b'52004' b'66564' b'70516' b'94472' b'1356' b'557064' b'36488' b'57152'
b'1424' b'2488' b'88' b'9140' b'909192' b'7712' b'6668' b'12556' b'81596'
b'65428' b'11200' b'54524' b'455536' b'65140' b'25128' b'10756' b'3668'
b'66460' b'14796' b'14756' b'8888' b'24836' b'80020' b'4240' b'28'
b'590676' b'300012' b'65812' b'4564' b'97792' b'63120' b'1560' b'24816'
b'142952' b'5988' b'65444' b'945984' b'7736' b'60928' b'6664' b'8500'
b'3056' b'67216' b'124' b'2280' b'5240' b'64596' b'73268' b'530580'
b'1232' b'9040' b'2688' b'83900' b'2192' b'16800' b'565672' b'3200'
b'538028' b'1284' b'8444' b'65016' b'5228' b'78180' b'12176' b'1452'
b'809012' b'2132' b'64616' b'27072' b'2456' b'80024' b'914476' b'67328'
b'76' b'6612' b'51740' b'118652' b'10092' b'75352' b'949352' b'2352'
b'24' b'12456' b'82836' b'6364' b'7152' b'51504' b'44480' b'917780'
b'1276' b'65932' b'762880' b'2520' b'908208' b'2440' b'102400' b'10296'
b'300016' b'51292' b'8388' b'8104' b'4192' b'11224' b'66476' b'965524'
b'2556' b'452164' b'10808' b'7668' b'989584' b'73276' b'738512' b'6032'
b'16676' b'11084' b'10792' b'3144' b'2540' b'32920' b'1324' b'65096'
b'4572' b'73244' b'16236' b'5688' b'22320' b'83872' b'14628' b'66760'
b'9416' b'5736' b'45644' b'8592' b'14524' b'603544' b'3460' b'5184'
b'76404' b'139872' b'2508' b'5080' b'4656' b'10036' b'6356' b'49756'
b'64088' b'1368' b'4816' b'455204' b'25716' b'6348' b'151936' b'56'
b'183996' b'91936' b'4948' b'454264' b'2632' b'2460' b'6452' b'65260'
b'6132' b'67028' b'2584' b'66376' b'41864' b'76200' b'9956' b'2412'
b'102272' b'61112' b'449588' b'5664' b'5084' b'6352' b'908236' b'36568'
b'2140' b'12540' b'71500' b'20' b'957420' b'54584' b'1184' b'3196'
b'4704' b'2304' b'3124' b'16456' b'41872' b'11940' b'3744' b'4408'
b'68028' b'84372' b'3236' b'65836' b'119632' b'2256' b'51672' b'65708'
b'31116' b'36312' b'309828' b'12600' b'25108' b'2512' b'160' b'24988'
b'11632' b'5312' b'10992' b'454504' b'11924' b'91280' b'24000' b'173052'
b'565772' b'8216' b'785820' b'22160' b'5576' b'41524' b'95160' b'67552'
b'1512' b'153472' b'5704' b'11560' b'168' b'80536' b'54988' b'71804'
b'5616' b'64952' b'63380' b'22252' b'84160' b'2676' b'823960' b'11848'
b'64228' b'555808' b'37684' b'83740' b'34380' b'64280' b'2704' b'49664'
b'60444' b'100308' b'2516' b'74700' b'10104' b'16600' b'7224' b'108'
b'1244' b'8252' b'4196' b'957296' b'11708' b'68196' b'45080' b'82604'
b'966160' b'962964' b'444016' b'561804' b'7492' b'10424' b'12436'
b'784484' b'7108' b'44412' b'592792' b'80344' b'955756' b'92232'
b'404856' b'988072' b'3276' b'9376' b'54920' b'17756' b'3736' b'1328'
b'766544' b'4812' b'66956' b'67976' b'2568' b'2504' b'33276' b'7984'
b'4328' b'776524' b'36504' b'66332' b'51580' b'4148' b'45796' b'80656'
b'92344' b'3468' b'989184' b'5636' b'57024' b'12532' b'5508' b'100664'
b'8960' b'54976' b'89560' b'4688' b'108140' b'9408' b'5444' b'443584'
b'10896' b'3808' b'970944' b'10892' b'579468' b'6568' b'1248' b'24792'
b'36824' b'45440' b'964104' b'2296' b'16144' b'6156' b'8384' b'68460'
b'10428' b'4436' b'49552' b'3324' b'5232' b'80460' b'4668' b'11488'
b'85456' b'744292' b'51372' b'147572' b'51448' b'5056' b'538596' b'36444'
b'7880' b'583488' b'5920' b'64096' b'965600' b'59580' b'771140' b'110056'
b'52672' b'76768' b'18504' b'93044' b'140912' b'71004' b'776584' b'32756'
b'1212' b'286488' b'25132' b'79380' b'2732' b'41516' b'11160' b'272088'
b'2180' b'67364' b'42600' b'455356' b'36328' b'28352' b'968940' b'30696'
b'64664' b'829024' b'40360' b'181092' b'1572' b'8768' b'203036' b'757168'
b'2860' b'52724' b'12360' b'55172' b'71128' b'87020' b'3428' b'76920'
b'5104' b'966360' b'25116' b'582564' b'749048' b'32764' b'97980'
b'606420' b'1548' b'1304' b'737736' b'2288' b'6276' b'683160' b'75256'
b'9888' b'4956' b'51444' b'65904' b'5672' b'234984' b'68500' b'48664'
b'362508' b'36528' b'6224' b'4104' b'49452' b'3868' b'41584' b'105704'
b'77100' b'452160' b'2244']
Feature: top_3_60_day_non_fresh_product
Tensor: [b'8676' b'1284' b'65452' b'119632' b'1356' b'2624' b'968504' b'8180'
b'2572' b'5844' b'51504' b'64888' b'70312' b'10756' b'609960' b'2412'
b'1500' b'8500' b'1232' b'7152' b'65444' b'62732' b'7668' b'17756'
b'2144' b'1244' b'12556' b'2732' b'28360' b'2488' b'1424' b'244148'
b'1352' b'5464' b'12044' b'11424' b'66564' b'10208' b'1276' b'234900'
b'2132' b'2192' b'749048' b'2492' b'7108' b'48688' b'3056' b'1228' b'88'
b'14764' b'124' b'24' b'36528' b'6668' b'108104' b'2280' b'3464' b'70516'
b'1560' b'31112' b'1184' b'4816' b'41872' b'60944' b'100244' b'2860'
b'3720' b'66460' b'3880' b'48736' b'1516' b'10092' b'86780' b'2296'
b'52004' b'10104' b'2352' b'10956' b'966464' b'982392' b'490528' b'51448'
b'51784' b'404856' b'70784' b'6276' b'87036' b'64596' b'34664' b'11220'
b'3744' b'6212' b'2188' b'40' b'1452' b'65052' b'956856' b'8756' b'52652'
b'5872' b'73088' b'36584' b'57152' b'1312' b'60904' b'2456' b'234984'
b'65096' b'14808' b'6348' b'3200' b'559092' b'51292' b'14588' b'300012'
b'64636' b'57084' b'10808' b'56160' b'59580' b'19172' b'1240' b'2608'
b'75152' b'2508' b'42424' b'6156' b'80460' b'44480' b'66108' b'749928'
b'81684' b'2520' b'62252' b'2332' b'753992' b'37748' b'12136' b'6564'
b'25108' b'14628' b'3144' b'344412' b'51444' b'51372' b'1220' b'2552'
b'3092' b'7136' b'160' b'34580' b'67692' b'66476' b'399060' b'71132'
b'309828' b'2704' b'3484' b'14524' b'24816' b'17452' b'70444' b'12176'
b'135372' b'4564' b'277808' b'300016' b'1568' b'49712' b'610360' b'5080'
b'771140' b'49684' b'9040' b'2140' b'112988' b'26352' b'73276' b'258728'
b'949352' b'60856' b'2256' b'1252' b'12516' b'11944' b'16708' b'4780'
b'5616' b'7492' b'8408' b'68560' b'9628' b'1344' b'71500' b'7424'
b'964960' b'3808' b'76808' b'105632' b'67244' b'5228' b'66492' b'191112'
b'761164' b'4196' b'913772' b'36444' b'3608' b'67216' b'958324' b'8412'
b'12324' b'1456' b'8768' b'2452' b'84160' b'68304' b'5104' b'15920'
b'6352' b'5348' b'68276' b'6568' b'45528' b'28' b'7880' b'89688'
b'682264' b'24912' b'5672' b'12552' b'51752' b'20808' b'2588' b'4408'
b'181936' b'4704' b'97792' b'906288' b'3596' b'25128' b'84020' b'66568'
b'90236' b'557064' b'71864' b'331880' b'328828' b'86420' b'447028'
b'75124' b'5344' b'6132' b'51620' b'227660' b'115876' b'64088' b'596044'
b'2512' b'597420' b'36852' b'36504' b'199800' b'9504' b'8556' b'11664'
b'32772' b'33412' b'49760' b'3480' b'3816' b'442416' b'64952' b'48304'
b'65812' b'780480' b'10896' b'52492' b'33044' b'782860' b'7584' b'16536'
b'45612' b'4936' b'9956' b'766024' b'11512' b'8064' b'4524' b'10036'
b'48264' b'74704' b'51660' b'42476' b'11344' b'232832' b'2848' b'51716'
b'5704' b'6052' b'908244' b'7224' b'1368' b'24940' b'32936' b'20152'
b'989584' b'11836' b'2388' b'20' b'1740' b'16600' b'3488' b'65428'
b'22160' b'1756' b'11236' b'57004' b'211980' b'56804' b'8384' b'2208'
b'3920' b'450356' b'989708' b'91280' b'8388' b'577000' b'2440' b'357996'
b'77380' b'9544' b'64228' b'3476' b'64280' b'587804' b'557748' b'NA'
b'957176' b'12096' b'11848' b'55064' b'48412' b'1464' b'3736' b'9408'
b'967048' b'101384' b'444016' b'912344' b'65968' b'12636' b'3668'
b'251808' b'12716' b'4112' b'24988' b'682952' b'140004' b'577092' b'5236'
b'96208' b'501788' b'54464' b'6512' b'776584' b'60396' b'3344' b'785236'
b'455204' b'83872' b'1404' b'12544' b'908236' b'55256' b'65216' b'71004'
b'99868' b'7892' b'1304' b'488608' b'25116' b'108' b'47936' b'7736'
b'770056' b'65696' b'65828' b'5444' b'11432' b'6548' b'91128' b'110196'
b'36568' b'2416' b'9904' b'908208' b'56996' b'19356' b'786928' b'5944'
b'117412' b'4572' b'83132' b'597236' b'5136' b'75656' b'502228' b'54976'
b'16652' b'65892' b'67364' b'17528' b'75256' b'184192' b'66320' b'28344'
b'19740' b'8592' b'9056' b'754876' b'48608' b'7072' b'65972' b'3784'
b'583288' b'744460' b'82364' b'175656' b'12436' b'1556' b'5056' b'2516'
b'3124' b'7984' b'19192' b'52220' b'586936' b'149840' b'59640' b'7572'
b'125172' b'51556' b'496468' b'79384' b'2776' b'42912' b'24788' b'63196'
b'8260' b'30816' b'551076' b'81596' b'36848' b'81908' b'9756' b'499452'
b'7532' b'613884' b'42444' b'903488' b'59332' b'40396' b'770864' b'2268'
b'70636' b'3096' b'9376' b'8888' b'70292' b'485780' b'80776' b'11024'
b'1272' b'5240' b'538184' b'4880' b'67976' b'603544' b'64708' b'36824'
b'12272' b'86436' b'51416' b'200112' b'411544' b'14732' b'6392' b'957816'
b'4520' b'12360' b'45796' b'14560' b'5004' b'528252' b'51364' b'36396'
b'766544' b'49764' b'4948' b'51484' b'42612' b'67704' b'32800' b'12656'
b'78052' b'1736' b'5584' b'26828' b'913992' b'12676' b'6452' b'76'
b'1324' b'72060']
Feature: top_4_60_day_non_fresh_product
Tensor: [b'88' b'6212' b'1424' b'1500' b'118652' b'124' b'1352' b'174456'
b'300016' b'51448' b'41872' b'80536' b'66088' b'12552' b'6664' b'2732'
b'2508' b'2132' b'65336' b'66564' b'62080' b'14760' b'6364' b'17756'
b'19124' b'45520' b'66108' b'66460' b'3600' b'1284' b'81096' b'300012'
b'25108' b'949352' b'67704' b'1356' b'2556' b'99648' b'2412' b'12176'
b'2192' b'4408' b'63120' b'2416' b'34580' b'1452' b'2704' b'57164'
b'366352' b'173052' b'6380' b'1312' b'61448' b'5312' b'1560' b'1332'
b'12136' b'173436' b'11512' b'5080' b'78384' b'41864' b'71128' b'2144'
b'2460' b'60856' b'9676' b'90404' b'5228' b'444016' b'51504' b'84652'
b'79040' b'91384' b'36824' b'11472' b'293556' b'682264' b'119880'
b'453788' b'60944' b'49552' b'14628' b'503952' b'64616' b'11236' b'1208'
b'51412' b'7108' b'4816' b'1472' b'749048' b'65896' b'2304' b'331880'
b'5240' b'968576' b'65824' b'7152' b'3744' b'3056' b'61696' b'1596'
b'2492' b'450748' b'51304' b'482208' b'64596' b'5332' b'1276' b'9672'
b'12344' b'6844' b'530580' b'20' b'3200' b'598592' b'82836' b'2848'
b'67412' b'9456' b'12260' b'1344' b'908208' b'583488' b'2608' b'10892'
b'30888' b'66476' b'31116' b'213108' b'16600' b'1304' b'36488' b'5344'
b'113192' b'1228' b'3276' b'5736' b'10104' b'41780' b'2572' b'51736'
b'1232' b'9904' b'110032' b'36504' b'108104' b'4564' b'51672' b'56472'
b'65932' b'7500' b'3464' b'83272' b'3392' b'999344' b'70428' b'7104'
b'7224' b'10036' b'140036' b'52044' b'611000' b'16272' b'44392' b'76'
b'5104' b'174788' b'78436' b'91936' b'71440' b'40620' b'6156' b'1456'
b'987808' b'68236' b'8388' b'67976' b'28' b'1324' b'8500' b'41524'
b'60840' b'3532' b'3476' b'14732' b'49732' b'56136' b'3808' b'4160'
b'464592' b'2552' b'3144' b'104968' b'108' b'2568' b'71452' b'65016'
b'54436' b'6348' b'2180' b'160' b'42424' b'57152' b'10428' b'8216'
b'6132' b'66320' b'2456' b'196688' b'68572' b'49664' b'61064' b'7492'
b'8880' b'80068' b'45440' b'3920' b'8556' b'404832' b'5872' b'65444'
b'375516' b'12656' b'68276' b'7712' b'16800' b'6568' b'83872' b'3564'
b'3596' b'1184' b'68216' b'64228' b'48664' b'7508' b'36444' b'7560'
b'10792' b'244148' b'NA' b'761168' b'150100' b'65424' b'10092' b'64088'
b'811500' b'73244' b'948284' b'95648' b'50708' b'65492' b'780244'
b'81892' b'57496' b'949792' b'8896' b'66796' b'771296' b'3736' b'4192'
b'9956' b'454264' b'1432' b'43196' b'35228' b'1420' b'6352' b'4700'
b'5136' b'76588' b'48412' b'5236' b'4040' b'2208' b'168' b'120' b'22100'
b'4316' b'44092' b'67364' b'7668' b'9544' b'61112' b'65436' b'2512'
b'234900' b'27300' b'24' b'80460' b'357704' b'14524' b'5464' b'440168'
b'11488' b'48276' b'16668' b'60440' b'16544' b'18448' b'64664' b'109364'
b'467604' b'64136' b'746024' b'908244' b'26404' b'74704' b'21152' b'32'
b'12556' b'78308' b'66568' b'5688' b'65428' b'92164' b'597840' b'36820'
b'66448' b'43528' b'44416' b'51484' b'54976' b'11848' b'65216' b'71428'
b'124800' b'3236' b'3488' b'75124' b'63400' b'9040' b'1748' b'25116'
b'681956' b'908236' b'3480' b'146784' b'45244' b'164' b'60940' b'12636'
b'3468' b'36568' b'16696' b'51372' b'16176' b'60928' b'11664' b'52004'
b'10636' b'14532' b'65816' b'14500' b'904524' b'51716' b'2440' b'84160'
b'83740' b'50436' b'766024' b'22252' b'950768' b'64824' b'588048'
b'48704' b'175644' b'1212' b'79408' b'5404' b'736060' b'96216' b'51292'
b'184636' b'8644' b'78544' b'7884' b'36848' b'16536' b'67508' b'2352'
b'3904' b'1404' b'26804' b'41040' b'2516' b'4328' b'82996' b'3668'
b'190232' b'92668' b'489120' b'8720' b'7616' b'99288' b'502584' b'10384'
b'103364' b'77868' b'16168' b'70516' b'76144' b'152900' b'83900' b'25128'
b'8064' b'344412' b'8452' b'19740' b'48680' b'9408' b'1464' b'19172'
b'611596' b'8252' b'2236' b'908212' b'51752' b'514072' b'7080' b'60272'
b'65812' b'488608' b'75256' b'65916' b'11468' b'24916' b'21300' b'51436'
b'4380' b'8384' b'68468' b'8540' b'15920' b'51444' b'131140' b'55336'
b'6204' b'405768' b'1256' b'66452' b'31068' b'5444' b'48624' b'7716'
b'530380' b'182520' b'33108' b'903488' b'25784' b'66256' b'86140'
b'771140' b'1320' b'965648' b'10808' b'2860' b'7228' b'141012' b'7980'
b'86444' b'309828' b'1460' b'6968' b'595828' b'4936' b'24836' b'9376'
b'22188' b'41496' b'2140' b'430812' b'7876' b'99880' b'756964' b'373576'
b'4948' b'2248' b'28660' b'565660' b'3124' b'1220' b'9416' b'5508'
b'809012' b'50876' b'6392' b'2864' b'379780' b'1496' b'97408' b'12620'
b'64100' b'443368' b'64636' b'2296' b'573728' b'6240' b'1328' b'684088'
b'11344' b'117400' b'48936' b'1308' b'4520' b'279408' b'914476' b'2264'
b'66532' b'753992' b'36836' b'24932' b'326180' b'9872' b'1548' b'226108'
b'64096' b'17392' b'42912' b'2776' b'52220' b'7176' b'5008' b'957504'
b'10832' b'5476' b'45364' b'56540' b'67688' b'103080' b'79644' b'819108'
b'533496' b'3720' b'6612' b'4908' b'2532' b'12672' b'150352' b'749112']
Feature: top_5_60_day_non_fresh_product
Tensor: [b'1284' b'12092' b'1500' b'64616' b'31068' b'1424' b'777928' b'757168'
b'2440' b'51504' b'54988' b'945672' b'65444' b'736296' b'193904' b'5240'
b'9740' b'1356' b'65932' b'1220' b'47936' b'2624' b'1212' b'124' b'1452'
b'949352' b'74704' b'3596' b'68204' b'300016' b'565168' b'16456' b'60904'
b'2492' b'6568' b'1232' b'68276' b'1572' b'565660' b'80460' b'2608'
b'2264' b'544116' b'64088' b'455204' b'87016' b'83740' b'19740' b'105744'
b'41872' b'9904' b'17756' b'3428' b'1456' b'88' b'12436' b'37752'
b'65016' b'2556' b'2256' b'113192' b'1228' b'7224' b'4196' b'34384'
b'97792' b'8500' b'152556' b'65436' b'66108' b'461164' b'51292' b'1352'
b'65828' b'5596' b'7152' b'2352' b'65140' b'79160' b'771140' b'64824'
b'56584' b'8216' b'6348' b'482992' b'2388' b'5812' b'50876' b'70364'
b'2572' b'98044' b'12032' b'10036' b'125284' b'22252' b'400124' b'566300'
b'78000' b'2704' b'9344' b'12136' b'82864' b'65496' b'66452' b'3668'
b'5508' b'6156' b'1620' b'2848' b'66312' b'946780' b'1568' b'443832'
b'11848' b'92232' b'914476' b'2132' b'2520' b'64704' b'2412' b'7720'
b'70624' b'6448' b'908236' b'3488' b'1312' b'956972' b'84720' b'44796'
b'80344' b'5624' b'2512' b'2516' b'54984' b'49664' b'5576' b'16160'
b'5444' b'374376' b'293556' b'3744' b'2540' b'51348' b'117136' b'76224'
b'81248' b'2140' b'8672' b'104796' b'9544' b'1276' b'3144' b'12656'
b'76564' b'184120' b'8464' b'25692' b'10428' b'150304' b'171184' b'55380'
b'10412' b'765808' b'599032' b'52048' b'2304' b'24584' b'64748' b'908244'
b'1332' b'24788' b'193816' b'43260' b'8384' b'57152' b'595936' b'4948'
b'3476' b'14584' b'3464' b'71428' b'50552' b'9320' b'583488' b'6276'
b'2456' b'767916' b'65248' b'66376' b'117440' b'63016' b'28' b'65836'
b'60856' b'10908' b'27220' b'7816' b'78876' b'4408' b'3124' b'3920'
b'16168' b'2192' b'76764' b'44412' b'65420' b'5736' b'2504' b'3408'
b'48936' b'105900' b'1184' b'11444' b'66332' b'65336' b'5616' b'7220'
b'2568' b'3604' b'5080' b'12400' b'70516' b'51372' b'24' b'60388' b'3824'
b'17724' b'6132' b'207580' b'6072' b'581856' b'36348' b'8388' b'2688'
b'110204' b'12176' b'170588' b'2188' b'32936' b'2392' b'20828' b'784180'
b'66040' b'1528' b'19928' b'66564' b'4800' b'45612' b'5236' b'NA' b'7492'
b'103984' b'9956' b'50444' b'81596' b'566344' b'65096' b'7216' b'2148'
b'116456' b'1244' b'32' b'9676' b'185588' b'4564' b'184704' b'66320'
b'88408' b'3060' b'9504' b'1328' b'11656' b'3200' b'66460' b'105716'
b'60444' b'71484' b'51752' b'5664' b'955608' b'55476' b'1404' b'2332'
b'8556' b'54976' b'85388' b'57116' b'785236' b'4508' b'40920' b'1560'
b'160' b'10092' b'8552' b'20' b'67976' b'7736' b'244148' b'3720' b'3424'
b'82996' b'42488' b'36848' b'7572' b'3444' b'1280' b'7668' b'70728'
b'44692' b'557344' b'33460' b'2732' b'51444' b'7108' b'11560' b'77096'
b'537452' b'33108' b'64252' b'1368' b'277808' b'60940' b'489216' b'2280'
b'557064' b'64096' b'6212' b'482220' b'108940' b'9408' b'61064' b'564856'
b'3460' b'3344' b'24836' b'813516' b'45652' b'51304' b'3808' b'2672'
b'78124' b'7880' b'65896' b'2108' b'16536' b'7704' b'908208' b'300012'
b'205884' b'45368' b'56088' b'65816' b'51320' b'84156' b'1200' b'16176'
b'14524' b'82836' b'84652' b'111260' b'989708' b'76' b'1240' b'11168'
b'466720' b'21676' b'7716' b'344412' b'3880' b'788508' b'31116' b'52460'
b'70396' b'36312' b'65524' b'3056' b'737736' b'91280' b'85036' b'115332'
b'3468' b'84016' b'25128' b'83048' b'4816' b'24940' b'813504' b'558208'
b'56624' b'84' b'4104' b'10184' b'57620' b'12544' b'247004' b'72'
b'51448' b'56076' b'452164' b'1552' b'4172' b'7336' b'19512' b'406252'
b'12204' b'36568' b'45204' b'51584' b'464592' b'108' b'51784' b'71440'
b'761392' b'7632' b'2508' b'10808' b'76388' b'19172' b'5228' b'12848'
b'66568' b'3600' b'1188' b'601492' b'42484' b'20880' b'79340' b'965524'
b'80216' b'19188' b'91936' b'48600' b'113520' b'16224' b'923888'
b'505332' b'112776' b'88112' b'5332' b'36712' b'614040' b'123432'
b'16236' b'1272' b'73088' b'55048' b'93620' b'66476' b'682264' b'2696'
b'11732' b'32960' b'3516' b'65832' b'3828' b'3564' b'56344' b'26068'
b'11836' b'93044' b'16600' b'30724' b'501784' b'16004' b'2208' b'84976'
b'214972' b'65488' b'42056' b'2860' b'1464' b'60396' b'66684' b'66348'
b'94992' b'7168' b'2552' b'501788' b'33276' b'64280' b'4148' b'5388'
b'102380' b'4328' b'43880' b'43612' b'99288' b'4572' b'4316' b'64136'
b'6452' b'17448' b'82368' b'2588' b'965604' b'11208' b'9868' b'4380'
b'12676' b'48264' b'95916' b'56848' b'14732' b'286488' b'598592' b'73408'
b'51484' b'7888' b'25116' b'66680' b'51864' b'5672' b'113544' b'12260'
b'430392' b'55620' b'51180' b'2296' b'14764' b'6700' b'66088' b'811132'
b'5984' b'11156' b'218024' b'102692' b'7084' b'3700' b'592792' b'2524'
b'2112' b'9804' b'34572' b'82364' b'2728' b'41516' b'67412' b'50872'
b'1440' b'6816' b'4700' b'766544' b'724144' b'14804' b'414468' b'10104'
b'1252' b'2144' b'66648' b'273356' b'1304' b'82192' b'186664' b'35688'
b'1460' b'2176' b'41532' b'51580' b'55064' b'757252' b'48556' b'75788'
b'6524' b'12056' b'15804' b'7712' b'2260' b'3480' b'59624' b'153888'
b'3220' b'25108' b'2584' b'196' b'16072' b'36488' b'7616' b'51616'
b'4524' b'16676' b'40456' b'4160' b'51876' b'6848' b'8920']
Feature: top_1_90_day_non_fresh_product
Tensor: [b'24980' b'124' b'1352' b'1356' b'2456' b'48736' b'2188' b'64632' b'6380'
b'10104' b'245488' b'2572' b'52004' b'51504' b'7152' b'66460' b'66564'
b'65336' b'8500' b'1284' b'97792' b'2496' b'64616' b'835436' b'1312'
b'1500' b'81596' b'65428' b'40908' b'88' b'1424' b'14796' b'1184'
b'399060' b'99648' b'2704' b'17756' b'5344' b'1560' b'4196' b'2492'
b'91936' b'7488' b'45244' b'77996' b'771148' b'1228' b'2412' b'24'
b'40412' b'813520' b'5080' b'10756' b'8592' b'2140' b'60396' b'64088'
b'14628' b'2732' b'31068' b'28' b'3488' b'1452' b'4408' b'1276' b'25128'
b'755440' b'7500' b'66320' b'4816' b'14828' b'7108' b'66376' b'57152'
b'20' b'2192' b'914476' b'48688' b'7736' b'118652' b'8388' b'1232'
b'8444' b'74700' b'36328' b'8172' b'6348' b'102400' b'16412' b'76224'
b'70516' b'274296' b'19740' b'51444' b'602856' b'16676' b'73268' b'3808'
b'10792' b'595936' b'908244' b'3276' b'4812' b'49712' b'67976' b'613884'
b'326180' b'66760' b'51372' b'3668' b'557064' b'8252' b'65904' b'7224'
b'603544' b'5184' b'76404' b'14764' b'2508' b'61112' b'65424' b'9384'
b'12176' b'173052' b'2132' b'3196' b'51292' b'11848' b'750000' b'36504'
b'1332' b'538028' b'2460' b'2584' b'64280' b'4564' b'2688' b'182520'
b'2256' b'2776' b'5104' b'45568' b'7608' b'65444' b'490528' b'82464'
b'44544' b'3200' b'84652' b'956856' b'2416' b'10808' b'51448' b'5704'
b'488608' b'63996' b'9416' b'87612' b'147824' b'82996' b'65932' b'12552'
b'502228' b'2556' b'10896' b'73244' b'25108' b'2516' b'14480' b'293556'
b'2860' b'64664' b'2512' b'955216' b'3480' b'83900' b'4016' b'24912'
b'908212' b'59608' b'48276' b'5444' b'96600' b'7176' b'908208' b'153472'
b'84372' b'87016' b'4704' b'6612' b'36568' b'95460' b'68196' b'3744'
b'737736' b'823960' b'83740' b'12556' b'60856' b'16600' b'949656'
b'56088' b'25132' b'48704' b'78672' b'65836' b'12540' b'539436' b'966160'
b'957504' b'41872' b'77908' b'7492' b'10424' b'300016' b'28376' b'581988'
b'64056' b'48900' b'16696' b'9956' b'8384' b'92232' b'365544' b'89136'
b'2452' b'5228' b'140004' b'588196' b'16116' b'9884' b'1420' b'3720'
b'501784' b'64824' b'215712' b'540040' b'1240' b'2540' b'83872' b'6212'
b'45796' b'110032' b'11924' b'92344' b'11760' b'1360' b'60940' b'233732'
b'5508' b'71428' b'5332' b'331880' b'442208' b'2112' b'6664' b'6156'
b'142452' b'66148' b'514072' b'3144' b'7984' b'144768' b'24836' b'537452'
b'68468' b'16144' b'51556' b'12544' b'51320' b'16748' b'66348' b'3028'
b'66796' b'24816' b'2144' b'3344' b'51716' b'597344' b'6568' b'81296'
b'49756' b'62916' b'4700' b'1304' b'746308' b'2520' b'2588' b'78052'
b'4948' b'92164' b'966464' b'60444' b'4160' b'108' b'17596' b'67244'
b'9544' b'77936' b'18948' b'76396' b'85348' b'12056' b'65016' b'121212'
b'3476' b'7712' b'2248' b'574696' b'5404' b'181092' b'65696' b'444016'
b'16' b'761164' b'33276' b'6452' b'36512' b'2280' b'6364' b'452164'
b'65488' b'54988' b'454264' b'583488' b'6072' b'97980' b'9040' b'3736'
b'36444' b'6276' b'83596' b'64112' b'60904' b'12360' b'3516' b'5476'
b'782776' b'234984' b'75788' b'49448' b'362508' b'20984' b'4560' b'97408'
b'183240' b'65288' b'306644' b'51868' b'12672' b'55240' b'56460']
Feature: top_2_90_day_non_fresh_product
Tensor: [b'24984' b'2572' b'65436' b'1352' b'1228' b'124' b'508720' b'8180'
b'65932' b'956856' b'2144' b'6816' b'1500' b'5344' b'1312' b'64824'
b'2192' b'66564' b'70516' b'94472' b'1356' b'11488' b'88' b'57152'
b'1424' b'2488' b'2732' b'64888' b'244148' b'51504' b'12556' b'11424'
b'54436' b'4116' b'11200' b'54524' b'4408' b'10756' b'3668' b'2492'
b'66460' b'14796' b'14756' b'7108' b'8888' b'24836' b'19420' b'4240'
b'28' b'66376' b'300012' b'65812' b'1284' b'12136' b'24' b'63120' b'6668'
b'1184' b'142952' b'78384' b'40456' b'945984' b'7736' b'6664' b'444016'
b'2860' b'2584' b'67216' b'3880' b'5240' b'64596' b'66760' b'73268'
b'530580' b'1232' b'9040' b'2688' b'84720' b'503952' b'2352' b'538028'
b'8444' b'51784' b'404856' b'5228' b'6276' b'12176' b'1452' b'65824'
b'3744' b'5616' b'2132' b'64616' b'65140' b'65052' b'494860' b'80024'
b'455204' b'9904' b'73088' b'76716' b'2520' b'6612' b'51740' b'64252'
b'66476' b'10092' b'908236' b'8500' b'12456' b'82836' b'10808' b'6568'
b'1276' b'44480' b'917780' b'9408' b'2440' b'25128' b'110032' b'51292'
b'51672' b'8388' b'37748' b'11224' b'965524' b'2556' b'452164' b'7668'
b'989584' b'7224' b'64088' b'495964' b'6156' b'116452' b'9812' b'1272'
b'2704' b'3144' b'70444' b'32920' b'65096' b'10636' b'73244' b'139848'
b'5688' b'71004' b'83872' b'455536' b'14628' b'193816' b'9416' b'63016'
b'5056' b'8592' b'9956' b'14524' b'60856' b'10792' b'3460' b'67016'
b'1560' b'11160' b'4656' b'10036' b'49756' b'49664' b'4816' b'25716'
b'6348' b'682264' b'300016' b'67244' b'56' b'454264' b'36444' b'6452'
b'41872' b'67028' b'12204' b'41864' b'2412' b'66040' b'19928' b'6352'
b'5664' b'12552' b'5084' b'7492' b'36568' b'24912' b'2140' b'3200'
b'70292' b'66252' b'20' b'16600' b'54584' b'3196' b'4704' b'184704'
b'2256' b'3124' b'14480' b'68028' b'84372' b'3236' b'908208' b'3476'
b'35228' b'119632' b'4508' b'65708' b'31116' b'213108' b'309828' b'12600'
b'67364' b'5736' b'160' b'11632' b'5080' b'65444' b'90296' b'27300'
b'60192' b'48304' b'565772' b'44692' b'2540' b'31120' b'746452' b'64228'
b'60440' b'41524' b'95160' b'76364' b'216276' b'65244' b'2516' b'5704'
b'68572' b'80536' b'48264' b'54988' b'71804' b'2508' b'64952' b'2460'
b'22252' b'84160' b'5076' b'44416' b'11848' b'555808' b'37684' b'25132'
b'34380' b'17196' b'24940' b'60444' b'100308' b'6356' b'4564' b'22160'
b'74700' b'10104' b'25116' b'2208' b'450356' b'1244' b'8172' b'31112'
b'4196' b'3056' b'146784' b'957448' b'65896' b'1368' b'11708' b'68196'
b'51716' b'45080' b'82604' b'913728' b'962964' b'4160' b'55064' b'12436'
b'7608' b'175644' b'80344' b'955756' b'25108' b'3468' b'988072' b'3276'
b'771140' b'54920' b'83740' b'16536' b'25136' b'100832' b'1404' b'108'
b'6132' b'2304' b'2568' b'2504' b'17756' b'501788' b'776524' b'36504'
b'785236' b'66956' b'66332' b'4192' b'4148' b'91720' b'51372' b'98160'
b'99868' b'8216' b'91936' b'2456' b'12532' b'100664' b'8960' b'54976'
b'11432' b'60708' b'5444' b'443584' b'67688' b'3808' b'970944' b'8384'
b'579468' b'3828' b'41032' b'567196' b'5136' b'964104' b'2296' b'501784'
b'5464' b'51444' b'65836' b'65892' b'4948' b'3324' b'11560' b'66320'
b'17596' b'1516' b'64280' b'48608' b'4668' b'85456' b'955168' b'51448'
b'3736' b'7880' b'746316' b'64096' b'65664' b'965600' b'120700' b'59580'
b'51556' b'32' b'14732' b'52672' b'6212' b'76768' b'12380' b'9376'
b'32756' b'30816' b'286488' b'5324' b'3596' b'5872' b'586936' b'2180'
b'52004' b'455356' b'59332' b'50876' b'968940' b'70636' b'64664'
b'829024' b'40360' b'281424' b'7572' b'1572' b'11024' b'10424' b'12360'
b'603544' b'3060' b'71128' b'60940' b'76920' b'51320' b'966360' b'582564'
b'749048' b'51348' b'766544' b'55156' b'1380' b'2264' b'683160' b'4956'
b'65904' b'83900' b'48664' b'4172' b'67704' b'6224' b'5236' b'3868'
b'41584' b'12676' b'105704' b'452160' b'14584' b'1324' b'150352' b'98496']
Feature: top_3_90_day_non_fresh_product
Tensor: [b'6348' b'6212' b'65452' b'119632' b'1356' b'2492' b'1352' b'24044'
b'2572' b'76224' b'41872' b'9396' b'51504' b'70312' b'10756' b'5240'
b'2412' b'1500' b'65932' b'1232' b'3440' b'65444' b'557064' b'7668'
b'17756' b'2144' b'949352' b'12556' b'9140' b'66108' b'300012' b'7712'
b'60904' b'1424' b'25108' b'66564' b'10208' b'1276' b'12176' b'65140'
b'63120' b'749048' b'2256' b'1284' b'2704' b'57164' b'2488' b'3056'
b'12436' b'1228' b'590676' b'5312' b'14764' b'8228' b'88' b'4196' b'5080'
b'97792' b'31112' b'71128' b'1184' b'9676' b'124' b'60928' b'65828'
b'113404' b'9956' b'66460' b'7152' b'11472' b'8500' b'1516' b'682264'
b'119880' b'86780' b'7108' b'10428' b'16800' b'5228' b'51660' b'5344'
b'490528' b'51448' b'28' b'2304' b'78180' b'87036' b'82864' b'4104'
b'2732' b'2188' b'40' b'81644' b'1452' b'956856' b'482208' b'2192'
b'52652' b'9672' b'36584' b'76' b'766544' b'234984' b'75352' b'65424'
b'7224' b'6568' b'51752' b'51292' b'12552' b'84720' b'17916' b'6364'
b'49664' b'59580' b'2552' b'1240' b'54984' b'6156' b'42424' b'10104'
b'2520' b'80460' b'44480' b'749928' b'81684' b'1560' b'2456' b'62252'
b'92344' b'753992' b'7500' b'6452' b'913728' b'9544' b'51444' b'70428'
b'1220' b'9344' b'10036' b'496084' b'234900' b'2132' b'399060' b'64220'
b'24812' b'5104' b'174788' b'14524' b'2540' b'9040' b'4564' b'277808'
b'300016' b'21912' b'771140' b'49684' b'10288' b'60840' b'5736' b'45644'
b'49732' b'25128' b'73276' b'49760' b'2608' b'60944' b'2568' b'71452'
b'5616' b'54436' b'16104' b'3144' b'7492' b'4520' b'57152' b'6132'
b'3200' b'1344' b'83740' b'51648' b'68572' b'90864' b'7424' b'8880'
b'5444' b'3808' b'76808' b'24' b'151936' b'51372' b'8388' b'183996'
b'4948' b'41516' b'913772' b'2632' b'68276' b'2556' b'958324' b'12780'
b'83872' b'2452' b'84160' b'19740' b'4816' b'52004' b'3488' b'102272'
b'449588' b'45612' b'12324' b'60444' b'7880' b'103980' b'64824' b'5672'
b'12540' b'71500' b'1528' b'2588' b'4408' b'917948' b'4704' b'9904'
b'95648' b'32' b'65492' b'16456' b'90236' b'66476' b'328828' b'86420'
b'771296' b'3736' b'4192' b'34608' b'44984' b'64276' b'5136' b'12532'
b'5236' b'10828' b'40920' b'11224' b'12136' b'2512' b'24988' b'36568'
b'65436' b'10992' b'42488' b'91280' b'1404' b'64952' b'65812' b'3824'
b'41864' b'3744' b'22160' b'7584' b'16544' b'64664' b'537452' b'54044'
b'1512' b'11512' b'7736' b'8064' b'168' b'91936' b'78308' b'4300'
b'63380' b'2848' b'2516' b'20000' b'5704' b'6052' b'54976' b'1368'
b'5688' b'20152' b'124800' b'2352' b'3668' b'2860' b'75124' b'63400'
b'838576' b'16600' b'65428' b'105900' b'1756' b'11236' b'40476' b'119244'
b'949832' b'67276' b'2508' b'1200' b'454264' b'48736' b'8252' b'2440'
b'10636' b'11732' b'14500' b'904524' b'587804' b'NA' b'957176' b'561804'
b'10808' b'22252' b'64088' b'65524' b'588048' b'44412' b'592792' b'2676'
b'68392' b'8920' b'36444' b'404856' b'81328' b'66568' b'65968' b'1324'
b'10896' b'57620' b'12544' b'12716' b'64280' b'452164' b'20' b'2624'
b'44888' b'577092' b'66956' b'67976' b'3484' b'96208' b'4328' b'76588'
b'6512' b'776584' b'19172' b'55256' b'65216' b'77868' b'16168' b'112988'
b'4524' b'51740' b'116388' b'77908' b'488608' b'12580' b'25116' b'47936'
b'8452' b'770056' b'65696' b'89560' b'11944' b'4688' b'34384' b'9496'
b'76732' b'108140' b'9408' b'6628' b'908208' b'60940' b'19356' b'786928'
b'75256' b'60856' b'11468' b'21300' b'83132' b'597236' b'1312' b'24792'
b'45440' b'603544' b'16676' b'48852' b'16652' b'68460' b'12184' b'1552'
b'66684' b'2280' b'91696' b'5232' b'8592' b'82836' b'102380' b'65972'
b'33108' b'82364' b'147572' b'70516' b'15920' b'3124' b'66960' b'19192'
b'5920' b'965648' b'586936' b'2776' b'149840' b'59640' b'7228' b'7572'
b'64596' b'110056' b'86444' b'2180' b'24788' b'48952' b'18504' b'93044'
b'8260' b'1212' b'551076' b'81596' b'81908' b'9756' b'65672' b'914116'
b'74752' b'613884' b'74704' b'67364' b'42600' b'903488' b'32756' b'36328'
b'75792' b'2268' b'11432' b'30696' b'12620' b'2728' b'2296' b'982568'
b'404584' b'36504' b'13776' b'757168' b'52724' b'450340' b'4880' b'32920'
b'3920' b'87020' b'3428' b'54980' b'411544' b'2416' b'606420' b'1548'
b'9872' b'57588' b'8616' b'1304' b'957816' b'11560' b'9888' b'7176'
b'5004' b'528252' b'16512' b'51364' b'2140' b'36396' b'37752' b'68500'
b'51484' b'2584' b'331880' b'533496' b'12656' b'78052' b'7616' b'1224'
b'557344' b'26828' b'45620' b'913992' b'400992' b'108']
Feature: top_4_90_day_non_fresh_product
Tensor: [b'88' b'1284' b'1424' b'1500' b'31068' b'2624' b'968504' b'82084'
b'65444' b'576656' b'51504' b'11684' b'66088' b'12552' b'3596' b'63124'
b'1312' b'1356' b'8500' b'2132' b'47936' b'65812' b'28' b'62080' b'14760'
b'1244' b'17756' b'139988' b'909192' b'120828' b'16456' b'7176' b'6568'
b'300012' b'25128' b'65016' b'2492' b'2412' b'234900' b'455536' b'81596'
b'2416' b'105744' b'9904' b'66460' b'64664' b'1456' b'124' b'1352'
b'1560' b'8388' b'66564' b'173436' b'24816' b'9344' b'3124' b'70516'
b'1276' b'445108' b'31112' b'60856' b'4816' b'41872' b'90404' b'108104'
b'2352' b'3720' b'79040' b'771140' b'2280' b'48736' b'293556' b'2572'
b'6204' b'65428' b'12672' b'2296' b'2192' b'64616' b'10104' b'565672'
b'1208' b'10956' b'400124' b'749048' b'2704' b'70784' b'5240' b'34664'
b'48756' b'71428' b'5508' b'2452' b'3056' b'4408' b'2848' b'8756' b'2440'
b'64596' b'160' b'5872' b'67328' b'914476' b'2908' b'2456' b'62996'
b'264156' b'949352' b'9456' b'12260' b'4692' b'908208' b'2608' b'65932'
b'30888' b'66476' b'957296' b'57084' b'2144' b'65828' b'4196' b'36488'
b'89384' b'113192' b'75152' b'7224' b'2348' b'6348' b'79960' b'1232'
b'4572' b'2520' b'4564' b'2332' b'8104' b'782860' b'6564' b'8444'
b'83272' b'54524' b'9376' b'999344' b'51372' b'7104' b'1212' b'3144'
b'3092' b'738512' b'55380' b'611000' b'599536' b'944220' b'57152'
b'51784' b'2496' b'17452' b'20' b'193816' b'24' b'50876' b'3200' b'22308'
b'3476' b'7108' b'64088' b'65816' b'911360' b'12556' b'11848' b'3484'
b'9956' b'55620' b'3744' b'14732' b'14796' b'12176' b'36824' b'5080'
b'12664' b'78876' b'3488' b'595828' b'9780' b'11944' b'76764' b'584428'
b'6364' b'68560' b'10428' b'245488' b'6356' b'97080' b'19740' b'7492'
b'7220' b'60940' b'68512' b'2508' b'51292' b'404832' b'60388' b'5228'
b'66492' b'191112' b'7632' b'2732' b'908236' b'2568' b'331880' b'67216'
b'1228' b'7712' b'16800' b'8768' b'3564' b'76200' b'1452' b'68216'
b'24916' b'1528' b'7508' b'5348' b'7560' b'45528' b'NA' b'34740' b'89688'
b'44484' b'20808' b'65424' b'957420' b'1184' b'9676' b'70312' b'68572'
b'84020' b'57496' b'949792' b'4948' b'36388' b'8300' b'75124' b'60444'
b'5344' b'601112' b'227660' b'99992' b'2256' b'70476' b'2512' b'65836'
b'6352' b'4700' b'551012' b'36852' b'36504' b'16600' b'9504' b'65096'
b'120' b'51580' b'44092' b'7668' b'36444' b'32772' b'244148' b'61112'
b'33412' b'300016' b'48704' b'11924' b'1220' b'24000' b'2516' b'3668'
b'8216' b'10896' b'52492' b'440168' b'11488' b'785820' b'33044' b'5576'
b'51716' b'16536' b'18448' b'4524' b'16676' b'64136' b'86924' b'67552'
b'2148' b'277808' b'205884' b'88716' b'11560' b'65672' b'10036' b'66568'
b'44480' b'11344' b'92164' b'65148' b'36820' b'908212' b'55064' b'908244'
b'51484' b'65216' b'32936' b'2488' b'922404' b'3236' b'541232' b'9040'
b'1740' b'5236' b'10808' b'81512' b'1332' b'3480' b'71080' b'45244'
b'56804' b'64340' b'8556' b'592792' b'82108' b'12636' b'989708' b'51444'
b'14524' b'14480' b'577000' b'357996' b'66348' b'60928' b'11768' b'64228'
b'21676' b'6132' b'5704' b'6212' b'557748' b'50436' b'766024' b'538028'
b'48412' b'1464' b'3736' b'784484' b'967048' b'36568' b'1240' b'444016'
b'79408' b'7936' b'912344' b'111260' b'813504' b'122128' b'251808'
b'247004' b'7884' b'4112' b'36848' b'1328' b'25108' b'8252' b'78308'
b'96684' b'22848' b'4812' b'41040' b'7984' b'82996' b'87016' b'190232'
b'92668' b'3344' b'455204' b'8720' b'7616' b'71440' b'12544' b'67704'
b'71004' b'404856' b'53648' b'816152' b'579468' b'57024' b'61588' b'6156'
b'2688' b'6548' b'611596' b'110196' b'45572' b'51752' b'179880' b'64096'
b'93620' b'488608' b'5944' b'10892' b'11732' b'32960' b'3516' b'117412'
b'5688' b'40456' b'598956' b'11772' b'502228' b'959932' b'54976'
b'214972' b'5476' b'104968' b'49552' b'405768' b'4104' b'7168' b'501788'
b'5444' b'113056' b'5388' b'80460' b'214408' b'530380' b'4328' b'43684'
b'583288' b'175656' b'67244' b'12436' b'1556' b'25784' b'42424' b'48268'
b'52220' b'7584' b'544116' b'2860' b'12676' b'11044' b'6612' b'14764'
b'65436' b'82852' b'2776' b'1460' b'4364' b'63380' b'4464' b'76' b'2140'
b'7876' b'87424' b'65904' b'272088' b'561920' b'15920' b'80780' b'753992'
b'603544' b'2584' b'11156' b'83900' b'770864' b'76132' b'543076'
b'379780' b'3096' b'97408' b'51556' b'4248' b'64100' b'34572' b'64636'
b'70292' b'41516' b'5104' b'80776' b'121356' b'770056' b'3880' b'1272'
b'538184' b'766544' b'50848' b'3460' b'12656' b'86436' b'54316' b'76252'
b'200112' b'279408' b'83872' b'2264' b'66532' b'32764' b'36836' b'2152'
b'24912' b'326180' b'35688' b'2288' b'75928' b'7888' b'74812' b'75256'
b'45796' b'14560' b'5008' b'957504' b'448748' b'78384' b'2436' b'45364'
b'56540' b'67688' b'737736' b'36528' b'11468' b'1344' b'54988' b'56'
b'49452' b'586028' b'76220' b'16696' b'61428' b'2244']
Feature: top_5_90_day_non_fresh_product
Tensor: [b'2192' b'12092' b'1500' b'2572' b'118652' b'1424' b'777928' b'922968'
b'51580' b'3596' b'54988' b'13860' b'25128' b'736552' b'6664' b'12656'
b'88' b'1220' b'65336' b'2624' b'62732' b'4408' b'1452' b'949352' b'6364'
b'85648' b'2872' b'300016' b'66460' b'3600' b'1284' b'2492' b'4192'
b'1232' b'65428' b'1560' b'541712' b'80460' b'2608' b'99648' b'54980'
b'64088' b'455204' b'87016' b'66564' b'34580' b'14760' b'5672' b'75124'
b'12324' b'173052' b'3344' b'1276' b'1356' b'2552' b'16160' b'817352'
b'113192' b'97792' b'36528' b'4112' b'1352' b'45612' b'5988' b'41864'
b'73540' b'2144' b'12640' b'51292' b'11220' b'10756' b'17680' b'464592'
b'9792' b'7152' b'84652' b'21676' b'1228' b'3488' b'56584' b'2352'
b'2556' b'6348' b'6212' b'2388' b'16524' b'49552' b'19740' b'9416'
b'51504' b'98044' b'1184' b'12032' b'3200' b'982392' b'2132' b'1472'
b'2540' b'10104' b'331880' b'6548' b'124696' b'3744' b'6156' b'3828'
b'1620' b'4284' b'1596' b'450748' b'51304' b'124' b'59868' b'11848'
b'92232' b'12344' b'65696' b'3120' b'60904' b'598592' b'66348' b'2848'
b'65096' b'14808' b'12436' b'6448' b'559092' b'10892' b'450624' b'64636'
b'64952' b'16600' b'56160' b'44796' b'5344' b'108' b'3476' b'1312'
b'16456' b'2732' b'1368' b'9676' b'5444' b'10296' b'36504' b'293556'
b'9504' b'8556' b'14628' b'67688' b'10808' b'4656' b'7136' b'5652'
b'64664' b'10412' b'16272' b'44392' b'5576' b'10036' b'51448' b'7224'
b'24584' b'78436' b'91936' b'24788' b'9408' b'1324' b'4816' b'4948'
b'565168' b'4572' b'16236' b'94352' b'12136' b'2508' b'6844' b'169888'
b'205884' b'41524' b'66376' b'65524' b'51660' b'66760' b'65260' b'186656'
b'906284' b'538028' b'3144' b'6220' b'3920' b'24' b'12516' b'31112'
b'16708' b'11444' b'4196' b'41872' b'65420' b'2504' b'31068' b'8216'
b'12848' b'66320' b'196688' b'61064' b'28' b'964960' b'45440' b'3532'
b'65444' b'99956' b'5688' b'5704' b'957448' b'375516' b'43976' b'207580'
b'48408' b'908212' b'3608' b'3124' b'16268' b'45600' b'12176' b'2392'
b'2568' b'68304' b'84372' b'8384' b'45572' b'15920' b'8500' b'2704'
b'65436' b'8388' b'10792' b'6032' b'7876' b'NA' b'4520' b'761168'
b'34748' b'50444' b'81596' b'37576' b'42912' b'10092' b'6568' b'32'
b'73244' b'51444' b'948284' b'11224' b'4708' b'50708' b'604164' b'66568'
b'1328' b'11940' b'11656' b'2440' b'28864' b'105716' b'600900' b'9956'
b'454264' b'7980' b'43196' b'35224' b'76776' b'5184' b'597420' b'54976'
b'76588' b'944116' b'199800' b'79608' b'22100' b'32936' b'357996'
b'945672' b'9544' b'3424' b'234900' b'454504' b'1380' b'2412' b'53668'
b'36312' b'45568' b'1304' b'203404' b'84852' b'3464' b'81016' b'6612'
b'7108' b'11560' b'2248' b'33108' b'64252' b'20' b'5228' b'603828'
b'7492' b'74704' b'60444' b'66332' b'2280' b'67644' b'66476' b'809012'
b'64096' b'42476' b'108940' b'597840' b'2408' b'14560' b'3460' b'2676'
b'5136' b'1332' b'14500' b'3056' b'65836' b'24940' b'17772' b'989584'
b'97408' b'78124' b'7880' b'3808' b'16536' b'551012' b'1748' b'300012'
b'7572' b'908236' b'1572' b'2688' b'174788' b'211980' b'94160' b'65816'
b'7712' b'3444' b'36568' b'1496' b'16176' b'66088' b'77380' b'11168'
b'11664' b'89980' b'957296' b'344412' b'48600' b'3880' b'586936' b'57152'
b'244148' b'24356' b'2180' b'749048' b'54348' b'101384' b'452160'
b'115332' b'13824' b'10108' b'75584' b'16544' b'5404' b'3060' b'51372'
b'83048' b'99472' b'96216' b'36848' b'1488' b'9376' b'3384' b'506276'
b'55620' b'61112' b'908208' b'8768' b'25116' b'4172' b'682952' b'766544'
b'47852' b'60944' b'49664' b'488608' b'5236' b'51584' b'4328' b'33276'
b'91720' b'56804' b'71440' b'2348' b'60396' b'7176' b'681956' b'9040'
b'502584' b'65664' b'169984' b'9768' b'1456' b'989184' b'51752' b'113520'
b'68276' b'68252' b'36712' b'19172' b'8252' b'14580' b'40360' b'82836'
b'140256' b'66108' b'682264' b'4564' b'65916' b'12676' b'24916' b'3276'
b'4380' b'2860' b'11472' b'1248' b'338572' b'36444' b'160' b'592792'
b'48968' b'40456' b'2208' b'84976' b'1212' b'131140' b'273356' b'6204'
b'4704' b'48556' b'184192' b'60452' b'55048' b'65812' b'10428' b'582000'
b'1464' b'9056' b'754876' b'48624' b'7072' b'182520' b'207192' b'744292'
b'903488' b'82996' b'3284' b'64136' b'66256' b'42444' b'86140' b'452164'
b'8592' b'14764' b'48264' b'14584' b'6132' b'496468' b'968840' b'231860'
b'6968' b'84160' b'595828' b'73248' b'9344' b'51484' b'776584' b'5388'
b'60856' b'44412' b'65496' b'78408' b'113544' b'99880' b'2488' b'499452'
b'430392' b'51180' b'28660' b'118800' b'5080' b'56592' b'181912' b'168'
b'218024' b'3700' b'2524' b'3212' b'82364' b'100032' b'12056' b'485780'
b'94792' b'3736' b'65620' b'48852' b'64100' b'103952' b'4700' b'2140'
b'724144' b'67976' b'65140' b'14524' b'64824' b'7532' b'404856' b'16652'
b'82192' b'82248' b'2420' b'24932' b'65932' b'5096' b'20904' b'6392'
b'1548' b'537168' b'108812' b'17392' b'2776' b'8448' b'24836' b'1256'
b'966160' b'16696' b'59624' b'16624' b'49764' b'2188' b'5084' b'32800'
b'16072' b'36488' b'80424' b'3824' b'4524' b'76232' b'544076']
Feature: Total_spend
Tensor: [6.3630461e+04 1.7847666e+04 8.0024941e+03 6.4460698e+03 1.7453709e+04
4.4308350e+03 8.5073848e+03 5.4749465e+04 6.0934678e+03 2.6506469e+05
5.5260361e+03 5.0634831e+05 2.9048040e+03 4.8849832e+04 3.9899341e+03
2.4073496e+04 4.1265925e+05 6.9135029e+03 7.3950122e+03 2.0685727e+04
1.9158292e+05 9.9065342e+03 6.1226729e+03 4.2850259e+03 1.2993336e+05
3.5136179e+03 4.1555161e+03 7.0525367e+04 6.2798328e+04 2.1122303e+05
6.4703608e+03 9.9786416e+03 1.0580913e+05 3.6447086e+04 1.0918341e+04
4.9065840e+03 1.0415799e+04 2.9817361e+03 3.3996421e+03 1.5835410e+03
4.4613091e+03 7.0310161e+03 1.1209932e+04 3.9660102e+04 1.5659806e+05
2.1961225e+04 1.6722324e+04 1.0948554e+04 4.1280122e+03 7.7083921e+03
4.8530078e+04 8.0993882e+03 3.0368879e+03 4.1956021e+03 1.2900231e+04
3.1426523e+04 4.8013945e+04 3.8410740e+03 3.4923149e+03 1.0119710e+05
1.8235871e+04 1.4785452e+04 5.7138301e+03 4.5418408e+03 1.8478260e+03
1.7515484e+04 2.8042200e+03 5.9766748e+03 2.3800969e+04 4.7289150e+03
2.8409148e+04 2.1378240e+03 7.3997102e+04 4.9700512e+04 1.4057811e+04
1.5703983e+04 2.6644409e+03 8.1605342e+03 1.3649616e+04 2.9378654e+04
2.2111741e+03 1.6654590e+03 2.6117496e+04 2.2319370e+03 4.4717219e+04
1.3335660e+04 7.2050488e+03 2.1243168e+04 1.6617870e+03 2.4314939e+03
1.4315310e+03 1.3983948e+04 1.2577806e+04 1.0160154e+04 2.5487371e+03
7.1774731e+03 1.0491669e+04 1.6287489e+04 1.1454849e+04 1.3574151e+04
6.9803461e+04 7.7896260e+03 1.7157061e+03 2.2284541e+03 3.2972849e+03
7.5039945e+04 6.5606401e+03 1.6998120e+03 4.9700249e+03 1.3400452e+05
5.9882490e+03 1.4794749e+04 3.2037571e+03 2.0560607e+04 8.0513098e+02
1.1891664e+04 2.3322617e+04 4.0373369e+03 1.6833365e+04 2.5560062e+04
2.8665271e+03 2.5257023e+04 2.5618860e+03 4.7966221e+03 2.3469867e+04
3.9382191e+04 2.1625623e+04 5.0223149e+03 6.3071909e+03 1.4730840e+03
3.9326840e+04 1.1366408e+05 1.7988210e+03 9.6193623e+03 3.7484027e+04
8.4446553e+03 1.7439436e+04 2.3370229e+04 1.6928234e+04 3.1676373e+04
3.0247199e+04 4.3958970e+03 1.3936770e+03 2.2222387e+04 3.0671027e+04
2.9696040e+03 1.2683889e+04 7.5344580e+03 1.2277575e+04 3.2255459e+03
1.0179900e+03 1.6453395e+04 1.5992181e+04 1.7855244e+04 9.7990830e+03
5.5740601e+03 1.4874921e+04 1.3768902e+04 1.6253280e+04 1.9738890e+03
2.3301621e+04 2.3449771e+03 7.9380000e+03 9.9279717e+03 2.5734331e+03
1.2126756e+05 1.4799780e+03 3.2805630e+03 1.5987492e+04 3.9867930e+03
1.9269549e+04 2.3541066e+04 2.1637232e+04 2.4963578e+04 8.5394248e+03
1.9127098e+04 9.6441660e+03 1.2963330e+03 2.7537661e+03 1.7869680e+03
2.5480629e+04 5.4137070e+03 1.0540107e+04 1.4564160e+03 2.3307346e+04
1.1812321e+03 6.9923000e+04 9.5675400e+03 2.1890225e+05 1.4745330e+03
2.7027773e+04 1.0781550e+04 1.9208610e+03 1.7224740e+03 3.6135000e+03
5.0946238e+04 1.1503134e+04 2.0601324e+04 2.1955320e+03 1.6267140e+04
5.4060122e+03 3.5473140e+03 2.2800239e+03 2.6021205e+04 2.0494160e+04
8.9901445e+03 5.3138789e+03 1.2095901e+05 7.6217129e+03 2.7472445e+04
1.5722208e+04 1.8166859e+04 5.5486260e+03 1.3322045e+05 1.5044670e+03
2.0736521e+04 6.1443901e+03 2.0135709e+04 1.0527075e+04 3.3466951e+03
3.0901230e+03 1.5914611e+03 3.1364369e+04 2.1398850e+04 2.0927727e+04
3.9317246e+04 4.0517801e+04 4.0344148e+04 9.3457354e+03 5.5300322e+03
1.3312800e+03 1.6288821e+04 1.8186967e+04 1.4124978e+04 1.9823652e+04
1.4268060e+03 3.4942410e+03 3.4190953e+04 3.2438008e+04 2.3996133e+04
2.7008101e+03 2.1387959e+03 2.5440930e+03 1.0894950e+03 1.2862764e+04
4.2295699e+04 1.6813305e+04 1.1815857e+04 1.2211812e+04 5.7083218e+03
6.4922129e+03 4.4768359e+04 5.2830088e+03 1.7743248e+04 3.6460891e+03
3.5391150e+03 3.8823301e+03 1.2690783e+04 3.5549109e+04 2.5785404e+04
1.5673806e+04 2.1799891e+04 2.2000645e+04 1.2210516e+04 1.7239635e+04
1.8079371e+04 1.2288555e+04 6.9002100e+02 2.0930129e+03 8.5602783e+03
1.5107508e+04 6.5447965e+04 2.4787710e+03 2.0899980e+03 1.1025179e+03
8.2720439e+03 4.4599951e+03 2.1554460e+03 4.5672388e+03 3.7963081e+03
3.8340811e+03 1.1995902e+04 2.2953960e+03 4.7232297e+04 3.3095339e+03
1.1048517e+04 3.9547351e+03 2.4117930e+03 4.4293742e+04 6.9147469e+04
1.6935740e+04 2.3458303e+04 1.2322440e+03 2.0087117e+04 5.2606440e+03
1.5325803e+04 1.8669420e+03 5.3130059e+03 6.4220132e+03 2.5785901e+03
7.8225210e+03 1.3904595e+04 6.8783398e+02 3.3000300e+03 3.3332310e+03
4.0760371e+03 3.0056688e+04 2.7268857e+04 4.0367429e+03 2.7417329e+03
9.5470918e+03 3.3004351e+03 3.5625239e+03 2.8374299e+03 3.5057520e+03
1.0177929e+04 1.8500635e+04 1.4487300e+03 3.4329600e+03 1.3342518e+04
2.7136980e+03 4.4535869e+03 1.6344720e+03 3.8091689e+03 4.0800420e+03
1.0760526e+04 1.3185189e+04 5.6998530e+03 2.2979475e+04 1.9799055e+04
1.4203530e+03 2.6132581e+03 6.7487310e+03 1.4089068e+04 1.9273950e+03
1.1536740e+03 1.0479060e+04 7.4049453e+04 1.9926441e+04 8.0525698e+03
2.8364670e+03 7.3469702e+02 7.6553730e+03 2.2617900e+03 4.4650855e+04
1.2889008e+04 2.8927800e+03 1.5765210e+03 1.5707907e+04 5.7670469e+03
5.8961188e+04 1.0849950e+03 2.1077199e+04 8.3107803e+03 1.1358450e+04
6.6244678e+03 2.7672300e+03 1.3491810e+04 7.8442920e+03 7.9813711e+03
7.7972671e+03 1.0185327e+04 1.0000152e+04 9.1955156e+03 3.7176389e+03
1.2437010e+03 1.4885820e+03 4.7274516e+04 1.8914940e+03 3.1994424e+04
1.3032918e+04 3.5601867e+04 6.5477158e+03 1.4367330e+03 5.0291548e+03
8.8126924e+03 1.3473216e+04 2.7490410e+03 1.3805551e+03 2.0974994e+04
2.0205990e+03 8.8212871e+03 5.8471650e+03 1.7150850e+03 1.2267846e+04
2.2892400e+03 9.1740596e+03 9.6827399e+02 1.4749191e+04 6.6424500e+02
1.9198494e+04 3.8036195e+04 1.5859800e+03 2.1551399e+03 8.3142266e+03
1.8012320e+04 2.4913539e+04 2.8046971e+04 4.1707979e+03 1.8374760e+03
8.8223401e+02 1.4307480e+03 3.4782390e+03 3.1124492e+04 1.1367054e+04
7.6568940e+03 1.1650905e+04 2.2058010e+03 1.1388150e+04 1.4095350e+03
3.8484990e+03 2.7018035e+04 8.9519941e+03 6.1488091e+03 6.3395552e+03
2.4441750e+03 2.6114761e+03 1.0627290e+03 8.4496504e+03 1.7210970e+03
1.4891688e+04 8.1810718e+03 3.7128781e+04 9.1562852e+03 2.5017930e+03
2.2382549e+03 4.0922371e+03 1.4157846e+04 1.3602419e+03 1.6727887e+04
1.3930803e+04 4.0319280e+03 5.6990996e+04 3.2816790e+03 2.3248711e+03
3.6566785e+04 1.3727430e+03 4.8200220e+03 5.3907300e+03 3.1743359e+03
8.5340068e+03 4.0669919e+03 1.0171296e+04 6.7312529e+03 1.1475081e+04
7.9457041e+03 1.6779150e+03 9.2818799e+03 1.3978710e+03 9.1928477e+04
1.3727340e+04 2.6900640e+03 1.0123884e+04 5.2683208e+03 2.1822930e+04
1.9892789e+04 2.2559912e+04 1.0062540e+04 3.9573820e+04 8.0108133e+04
6.3766803e+02 8.5423496e+03 6.9073110e+03 3.8697184e+04 6.8405402e+02
2.0642697e+04 8.0755539e+04 2.8953793e+04 1.6248357e+04 4.6641152e+03
2.6585758e+04 8.9499424e+03 1.1655747e+04 2.8129465e+04 1.7048430e+03
3.0267307e+04 4.1731289e+03 1.9370709e+04 5.2424639e+03 9.6408899e+02
3.8525581e+03 1.8399951e+04 3.9192749e+03 1.2318571e+03 3.2103369e+04
2.5864470e+03 2.1794670e+03 1.4180589e+04 1.8565470e+03 1.0943370e+03
1.7026748e+04 1.1786229e+04 1.2740112e+04 2.9085659e+03 8.6764250e+04
9.1740508e+03 3.4333020e+03 6.8499180e+04 6.0856831e+03 2.6509861e+03
9.0038702e+02 8.6999404e+03 5.4486182e+03 1.9715570e+04 2.3016599e+03
1.1763225e+04 1.8268290e+03 6.1175518e+03 1.4068476e+04 9.8425800e+02
2.8635012e+04 1.3572054e+04 1.3986027e+04 1.0759707e+04 5.8332241e+03
1.7768178e+04 1.9501740e+03 1.9851660e+03 7.2273602e+02 4.1108491e+03
1.2163592e+05 8.1818462e+03 2.8470959e+03 2.6429293e+04 5.2010371e+03
1.6844580e+03 1.8064457e+04 3.1788540e+03 7.7898508e+04 1.3749831e+04
8.1744299e+02 2.2321908e+04 1.1569950e+03 1.3033116e+04 3.7556633e+04
1.0456704e+04 2.0602981e+03 1.6987679e+03 5.7158099e+02 1.7424324e+04
1.0710000e+04 3.5852490e+03 1.3980690e+03 1.1830320e+03 8.2129590e+03
1.1704563e+04 2.8341809e+03 1.5680430e+03 1.0477350e+03 1.3290389e+03
4.3983809e+03 2.6031509e+03 9.2517568e+03 8.0326982e+03 2.2275379e+04
1.0133370e+03 2.3861431e+03 7.3140571e+03 2.1036889e+04 8.2017178e+03
7.9247520e+03 1.5720570e+03 1.8526941e+04 1.2207780e+04 1.1066454e+04
4.5255962e+03 2.2020939e+04 1.1499750e+03 5.7737701e+02 3.7758601e+03
7.5925620e+03 3.1223537e+04 8.3903223e+03 1.6569540e+03 7.4589302e+03
1.3873482e+04 2.8453689e+04 4.5752939e+03 2.4961321e+03 8.8400702e+02
2.7262529e+03 1.0567467e+04 8.4709707e+03 1.4085090e+04 1.0682469e+04
2.7012348e+04 2.7933760e+04 1.0934523e+04 3.9751021e+03 6.3787051e+03
1.4683051e+03 1.1756250e+03 9.7632900e+03 5.7246572e+03 1.0141470e+03
3.5875620e+03 6.3489600e+02 9.3705303e+03 8.2735557e+03 1.1020626e+04
9.8261553e+03 2.1880566e+04 9.5356797e+03 3.7840680e+04 1.0750590e+03
1.4838714e+04 5.8719780e+03 2.3659919e+03 1.4252193e+04 1.5931026e+04
1.8214290e+03 5.1469380e+03 9.9329043e+03 1.4487318e+04 6.6422881e+03
1.7688662e+04 7.4994658e+03 2.1630779e+03 4.9674238e+03 1.3836870e+04
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Feature: Total_item
Tensor: [3381. 1880. 969. 889. 2372. 772. 1207. 1875. 993. 1438. 691. 1328.
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330. 319. 229. 530. 573. 679. 247. 499. 943. 896. 355. 606.
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441. 1197. 318. 425. 103. 347. 99. 130. 2231. 177. 111. 513.
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Feature: Total_unique_items
Tensor: [ 687. 323. 218. 166. 644. 227. 508. 373. 172. 818. 182. 776.
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254. 300. 436. 283. 580. 415. 86. 305. 233. 238. 122. 998.
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382. 177. 386. 89. 60. 346. 49. 61. 394. 493. 155. 103.
679. 239. 87. 242. 502. 75. 380. 317. 325. 205. 263. 244.
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260. 515. 46. 123. 473. 393. 119. 133. 98. 84. 39. 256.
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Feature: Total_unique_subclasses
Tensor: [236. 116. 101. 78. 200. 198. 139. 90. 240. 256. 84. 130. 155. 249.
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186. 65. 129. 121. 28. 241. 52. 106. 104. 100. 43. 44. 126. 233.
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190. 163. 112. 31. 12. 173. 9. 91. 188. 140. 196. 29. 114. 13.
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Feature: Total_unique_subdepartments
Tensor: [68. 42. 41. 34. 66. 55. 69. 48. 43. 84. 33. 36. 56. 39. 60. 75. 47. 77.
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52. 58. 45. 19. 59. 46. 31. 57. 65. 17. 26. 29. 21. 22. 28. 20. 27. 64.
85. 92. 76. 79. 32. 37. 13. 16. 25. 18. 81. 23. 72. 10. 9. 11. 14. 15.
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Feature: Min_spend
Tensor: [0. 0.54 0.36 0.054 0.189 0.414 0.351 0.45 0.117 0.558 0.288 0.846
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Feature: Max_spend
Tensor: [1.6155000e+03 8.0352002e+02 2.7288000e+02 9.8550003e+01 3.4425000e+02
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Feature: days_since_last_purchase
Tensor: [ 0. 2. 1. 32. 37. 82. 36. 4. 12. 40. 3. 9. 5. 8.
34. 16. 51. 43. 20. 13. 26. 45. 21. 35. 17. 22. 15. 42.
46. 30. 6. 7. 59. 41. 69. 44. 57. 28. 134. 10. 39. 38.
95. 98. 88. 23. 11. 58. 25. 66. 163. 90. 54. 14. 19. 24.
52. 50. 72. 62. 33. 64. 140. 49. 130. 76. 48. 89. 31. 170.
93. 139. 56. 67.]
Feature: days_since_first_purchase
Tensor: [241. 240. 239. 235. 229. 236. 178. 234. 223. 231. 226. 208. 232. 228.
237. 214. 157. 222. 194. 238. 166. 188. 230. 233. 193. 177. 221. 148.
164. 215. 185. 219. 200. 70. 77. 147. 218. 174. 220. 227. 199. 196.
213. 224. 173. 225. 169. 211. 126. 85. 195. 170. 165. 206. 205. 163.
192. 144. 113. 149. 139. 55. 210. 159. 132. 204. 136. 175. 191. 197.
201. 189. 158. 168. 212. 127. 125. 146. 80.]
Feature: spend_per_trx
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14.255388 11.525735 95.71853 56.12409 9.942617
35.172176 6.2244706 91.86794 81.1133 108.04536
96.334854 214.51535 93.48706 370.98706 10.644149
146.91795 58.138397 23.425663 141.11082 157.73293
18.03395 50.95978 98.34558 143.4388 66.42288
176.88663 74.99466 21.63078 49.67424 138.3687
40.29066 249.14445 35.16073 728.0223 14.916636
59.514454 27.484 14.823 193.49873 13.0914545
64.45591 61.869274 286.36972 60.114635 325.60532
173.90443 15.284388 34.93653 100.68364 4.664847
427.03192 8.056469 54.650204 16.742228 282.1101
137.59572 33.393898 179.40489 118.25044 128.03177
14.124562 188.65941 52.902374 118.261406 26.637562
10.407469 83.19169 64.11947 13.158093 75.49446
6.370863 134.24655 77.05203 6.839526 300.4108
147.7135 24.65081 54.859547 556.92737 92.53383
157.96136 110.71942 105.78458 23.30441 145.62929
154.26335 80.66451 16.719128 160.82846 80.96208
50.82635 110.30965 90.521805 75.55136 152.94542
5.848085 41.62481 37.69296 332.31494 30.895548
116.89887 20.805775 62.74326 17.17413 61.080193
193.58554 33.239902 154.34662 57.529644 163.81277
23.958967 23.294544 118.37935 136.91397 30.042294
80.88261 15.7028475 345.5821 135.75356 146.38255
261.5444 27.52601 103.35424 8.433692 212.66534
177.2466 136.90187 127.553535 9.3634615 342.84253
17.398384 153.78221 79.44023 55.485397 350.9922
63.4542 507.6457 24.6629 81.9966 221.3292
98.8533 204.4977 5.7945 180.1818 19.241
201.241 209.6775 98.814 85.7879 732.1047
137.7093 40.3046 34.1901 50.2922 145.1268
53.6129 126.31884 24.945776 117.05036 154.3686
132.38545 402.66434 35.53564 91.13936 122.42073
240.91028 11.755416 83.92255 60.268806 13.61925
63.04582 48.494762 57.24041 12.191011 531.1069
222.73088 230.81175 10.67942 313.94098 15.876307
7.157966 24.451977 191.53452 36.566284 459.91873
265.23932 84.843414 73.54252 105.866486 332.35562
17.977345 57.510933 25.989723 464.9764 102.50669
82.09748 71.022514 320.07776 87.012314 22.795862
48.55563 19.153465 7.9930463 48.84028 92.25011
87.242966 235.21239 24.829535 56.971466 40.70323
18.116686 83.31729 12.4332905 139.04675 201.2604
114.22821 315.5243 48.210884 18.972635 62.88861
248.01692 388.60876 167.60648 1001.7247 34.89565
12.573635 30.851152 197.22197 15.818824 12.700059
236.98196 54.281963 199.13474 71.12775 171.77528
102.657646 732.69696 56.899178 64.55593 34.987286
18.906643 575.0038 52.247463 97.84403 76.99586
74.755196 71.85091 63.507793 158.74493 71.76329
113.233665 70.08799 412.37665 26.141314 9.765
180.03275 15.791964 9.953133 94.63825 412.55035
24.281784 10.60894 79.75328 5.925951 155.64304
113.74057 199.37009 150.5284 72.02788 164.53427
130.82872 138.97734 122.028694 4.0209146 131.44017
49.78833 237.14671 23.60294 404.063 43.936222
303.25388 25.737112 168.35844 144.92644 773.0211
77.858 68.97522 92.76833 23.849667 62.41811
167.93 64.51278 32.65867 66.721 401.85956
131.59511 9.246333 40.04289 55.271 182.095
11.914556 418.86432 260.58276 135.19733 1.9096667
71.906624 36.8829 31.275 72.17089 50.973186
203.94528 44.60344 53.5338 8.60715 117.6561
80.75745 9.136687 146.94435 257.15576 304.51163
88.674866 86.66933 65.337975 187.08772 77.97678
88.55761 24.334633 25.098152 138.00304 153.47267
80.92538 87.70739 9.785962 34.168102 244.35968
58.824455 120.139175 15.690304 14.071443 9.133975
203.36354 18.63843 27.880177 451.3057 89.36647
4.984177 18.235846 40.608 95.955345 103.808075
92.463234 162.1762 112.24731 263.3639 16.696154
86.61796 9.991615 15.155192 493.06882 107.909424
119.76658 137.27931 87.13087 9.741974 225.7124
11.812792 26.995792 27.692299 15.254533 7.4714026
146.57785 19.522987 167.36096 15.273 71.39314
7.2113376 176.59227 42.800495 114.1373 259.4097
148.07922 227.76862 185.82573 134.1861 35.356617
34.81792 81.15217 314.93973 116.620224 43.456264
5.816842 16.869198 153.25247 78.28437 211.51729
831.02765 75.43871 85.2705 177.31906 17.93404
151.74367 181.75597 126.75288 39.96912 68.04828
7.99128 96.29712 66.53592 39.24732 137.148 ]
Feature: Items_per_trx
Tensor: [ 6. 4. 2. 5. 3. 16. 9. 12. 14. 8. 7. 11. 10. 21. 18. 15. 13. 19.
20. 17. 29.]
Feature: Unique_items_per_trx
Tensor: [ 2. 1. 3. 4. 5. 6. 9. 7. 8. 11. 10.]
class SimilarityLayer(tf.keras.layers.Layer):
def __init__(self, embedding_dim, temperature=1.0, **kwargs):
super(SimilarityLayer, self).__init__(**kwargs)
self.temperature = temperature
# Define projection layers
self.member_projection = tf.keras.layers.Dense(embedding_dim, activation='relu')
self.item_projection = tf.keras.layers.Dense(embedding_dim, activation='relu')
def call(self, member_embeddings, item_embeddings):
# Ensure both tensors have the same data type
dtype = tf.float32
member_embeddings = tf.cast(member_embeddings, dtype)
item_embeddings = tf.cast(item_embeddings, dtype)
# Project embeddings to the same dimension
member_embeddings = self.member_projection(member_embeddings)
item_embeddings = self.item_projection(item_embeddings)
# Normalize the embeddings
member_embeddings = tf.nn.l2_normalize(member_embeddings, axis=-1)
item_embeddings = tf.nn.l2_normalize(item_embeddings, axis=-1)
# Compute the similarity as the dot product
similarity = tf.matmul(member_embeddings, item_embeddings, transpose_b=True)
# Scale the similarity by temperature
similarity = similarity / self.temperature
# Return both the similarity and the projected embeddings
return similarity, member_embeddings, item_embeddings
class RetrievalModelWithTopK(tfrs.Model):
def __init__(self, member_model, item_model, similarity_model, k=10):
super().__init__()
self.member_model = member_model
self.item_model = item_model
self.similarity_model = similarity_model
self.k = 10
# Initialize the FactorizedTopK retrieval layer
self.top_k = tfrs.layers.factorized_top_k.BruteForce()
def retrieve_top_k(self, member_features, item_features):
# Get embeddings from the models
member_embeddings = self.member_model(member_features)
item_embeddings = self.item_model(item_features)
# Get similarity and projected embeddings from SimilarityLayer
similarity, projected_member_embeddings, projected_item_embeddings = self.similarity_model(member_embeddings, item_embeddings)
# Index the item embeddings with the retrieval layer using the projected embeddings
self.top_k.index(projected_item_embeddings)
# Get top-k items for the given member features using the projected embeddings
top_k_values, top_k_indices = self.top_k(projected_member_embeddings)
return top_k_values, top_k_indices
def compute_loss(self, member_features, item_features, training=False):
top_k_values, top_k_indices = self.retrieve_top_k(member_features, item_features)
# Further processing...
return top_k_values, top_k_indices
Similarity_model = SimilarityLayer(temperature=1, embedding_dim=64)
retrieval_model = RetrievalModelWithTopK(member_model,item_model,Similarity_model,k=10)
top_k_scores, top_k_indices = retrieval_model.compute_loss(member_features, item_features)
# Convert tensors to numpy arrays for easier reading
top_k_scores_np = top_k_scores.numpy()
top_k_indices_np = top_k_indices.numpy()
# Print the shape of the results
print("Top-k Scores Shape:", top_k_scores_np.shape)
print("Top-k Indices Shape:", top_k_indices_np.shape)
Shape of member_embeddings: (1000, 3) Shape of top_1_7_day_fresh_product feature_embeddings (before padding): (140, 3) Shape of top_1_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_7_day_fresh_product feature_embeddings (before padding): (170, 3) Shape of top_2_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_7_day_fresh_product feature_embeddings (before padding): (160, 3) Shape of top_3_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_7_day_fresh_product feature_embeddings (before padding): (150, 3) Shape of top_4_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_7_day_fresh_product feature_embeddings (before padding): (152, 3) Shape of top_5_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_15_day_fresh_product feature_embeddings (before padding): (143, 3) Shape of top_1_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_15_day_fresh_product feature_embeddings (before padding): (168, 3) Shape of top_2_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_15_day_fresh_product feature_embeddings (before padding): (169, 3) Shape of top_3_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_15_day_fresh_product feature_embeddings (before padding): (166, 3) Shape of top_4_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_15_day_fresh_product feature_embeddings (before padding): (154, 3) Shape of top_5_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_60_day_fresh_product feature_embeddings (before padding): (147, 3) Shape of top_1_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_60_day_fresh_product feature_embeddings (before padding): (170, 3) Shape of top_2_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_60_day_fresh_product feature_embeddings (before padding): (177, 3) Shape of top_3_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_60_day_fresh_product feature_embeddings (before padding): (174, 3) Shape of top_4_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_60_day_fresh_product feature_embeddings (before padding): (185, 3) Shape of top_5_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_90_day_fresh_product feature_embeddings (before padding): (136, 3) Shape of top_1_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_90_day_fresh_product feature_embeddings (before padding): (174, 3) Shape of top_2_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_90_day_fresh_product feature_embeddings (before padding): (163, 3) Shape of top_3_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_90_day_fresh_product feature_embeddings (before padding): (182, 3) Shape of top_4_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_90_day_fresh_product feature_embeddings (before padding): (180, 3) Shape of top_5_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_7_day_non_fresh_product feature_embeddings (before padding): (402, 3) Shape of top_1_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_7_day_non_fresh_product feature_embeddings (before padding): (487, 3) Shape of top_2_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_7_day_non_fresh_product feature_embeddings (before padding): (531, 3) Shape of top_3_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_7_day_non_fresh_product feature_embeddings (before padding): (561, 3) Shape of top_4_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_7_day_non_fresh_product feature_embeddings (before padding): (583, 3) Shape of top_5_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_15_day_non_fresh_product feature_embeddings (before padding): (401, 3) Shape of top_1_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_15_day_non_fresh_product feature_embeddings (before padding): (487, 3) Shape of top_2_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_15_day_non_fresh_product feature_embeddings (before padding): (523, 3) Shape of top_3_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_15_day_non_fresh_product feature_embeddings (before padding): (560, 3) Shape of top_4_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_15_day_non_fresh_product feature_embeddings (before padding): (602, 3) Shape of top_5_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_60_day_non_fresh_product feature_embeddings (before padding): (366, 3) Shape of top_1_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_60_day_non_fresh_product feature_embeddings (before padding): (457, 3) Shape of top_2_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_60_day_non_fresh_product feature_embeddings (before padding): (515, 3) Shape of top_3_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_60_day_non_fresh_product feature_embeddings (before padding): (541, 3) Shape of top_4_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_60_day_non_fresh_product feature_embeddings (before padding): (585, 3) Shape of top_5_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_90_day_non_fresh_product feature_embeddings (before padding): (352, 3) Shape of top_1_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_90_day_non_fresh_product feature_embeddings (before padding): (432, 3) Shape of top_2_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_90_day_non_fresh_product feature_embeddings (before padding): (495, 3) Shape of top_3_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_90_day_non_fresh_product feature_embeddings (before padding): (535, 3) Shape of top_4_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_90_day_non_fresh_product feature_embeddings (before padding): (558, 3) Shape of top_5_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of Total_spend normalized_values (before padding): (999,) Shape of Total_spend normalized_values (after padding): (1000,) Shape of Total_trx normalized_values (before padding): (219,) Shape of Total_trx normalized_values (after padding): (1000,) Shape of Total_item normalized_values (before padding): (680,) Shape of Total_item normalized_values (after padding): (1000,) Shape of Total_unique_items normalized_values (before padding): (453,) Shape of Total_unique_items normalized_values (after padding): (1000,) Shape of Total_unique_subclasses normalized_values (before padding): (231,) Shape of Total_unique_subclasses normalized_values (after padding): (1000,) Shape of Total_unique_subdepartments normalized_values (before padding): (79,) Shape of Total_unique_subdepartments normalized_values (after padding): (1000,) Shape of Min_spend normalized_values (before padding): (92,) Shape of Min_spend normalized_values (after padding): (1000,) Shape of Max_spend normalized_values (before padding): (681,) Shape of Max_spend normalized_values (after padding): (1000,) Shape of days_since_last_purchase normalized_values (before padding): (74,) Shape of days_since_last_purchase normalized_values (after padding): (1000,) Shape of days_since_first_purchase normalized_values (before padding): (79,) Shape of days_since_first_purchase normalized_values (after padding): (1000,) Shape of spend_per_trx normalized_values (before padding): (1000,) Shape of Items_per_trx normalized_values (before padding): (21,) Shape of Items_per_trx normalized_values (after padding): (1000,) Shape of Unique_items_per_trx normalized_values (before padding): (11,) Shape of Unique_items_per_trx normalized_values (after padding): (1000,) Shape of final_concatenated_tensor: (1000, 136) Shape of Item_embeddings: (34663, 3) Shape of Item_subclass feature_embeddings (before padding): (1374, 3) Shape of Item_subclass feature_embeddings (after padding): (34663, 3) Shape of Item_subdepartment feature_embeddings (before padding): (220, 3) Shape of Item_subdepartment feature_embeddings (after padding): (34663, 3) Shape of Total_price_7_days normalized_values (before padding): (4776,) Shape of Total_price_7_days normalized_values (after padding): (34663,) Shape of Itm_qty_7_days normalized_values (before padding): (272,) Shape of Itm_qty_7_days normalized_values (after padding): (34663,) Shape of Trx_count_7_days normalized_values (before padding): (217,) Shape of Trx_count_7_days normalized_values (after padding): (34663,) Shape of Total_price_15_days normalized_values (before padding): (7245,) Shape of Total_price_15_days normalized_values (after padding): (34663,) Shape of Itm_qty_15_days normalized_values (before padding): (434,) Shape of Itm_qty_15_days normalized_values (after padding): (34663,) Shape of Trx_count_15_days normalized_values (before padding): (346,) Shape of Trx_count_15_days normalized_values (after padding): (34663,) Shape of Total_price_60_days normalized_values (before padding): (13710,) Shape of Total_price_60_days normalized_values (after padding): (34663,) Shape of Itm_qty_60_days normalized_values (before padding): (951,) Shape of Itm_qty_60_days normalized_values (after padding): (34663,) Shape of Trx_count_60_days normalized_values (before padding): (738,) Shape of Trx_count_60_days normalized_values (after padding): (34663,) Shape of Total_price_90_days normalized_values (before padding): (16172,) Shape of Total_price_90_days normalized_values (after padding): (34663,) Shape of Itm_qty_90_days normalized_values (before padding): (1193,) Shape of Itm_qty_90_days normalized_values (after padding): (34663,) Shape of Trx_count_90_days normalized_values (before padding): (947,) Shape of Trx_count_90_days normalized_values (after padding): (34663,) Shape of final_concatenated_tensor: (34663, 21) Top-k Scores Shape: (1000, 10) Top-k Indices Shape: (1000, 10)
all_scores = tf.concat([tf.reshape(top_k_scores[m], [-1]) for m in range(top_k_scores.shape[0])], axis=0)
all_scores = all_scores.numpy() # Convert tensor to numpy array for analysis
import matplotlib.pyplot as plt
# Increase figure size
fig, ax = plt.subplots(figsize=(12, 6)) # Adjust width and height as needed
# Create the histogram
counts, bins, patches = ax.hist(all_scores, bins=50, edgecolor='black', linewidth=0.5, histtype='bar')
# Adjust bar width and thickness
for patch in patches:
patch.set_edgecolor('black') # Set edge color
patch.set_linewidth(1.5) # Thicker bar edges
# Optionally adjust the width here if needed
# Add data labels on top of each bar
for count, bin, patch in zip(counts, bins, patches):
# Calculate the height of the label
height = patch.get_height()
# Add the label with size 7 and color black
ax.text(patch.get_x() + patch.get_width() / 2, height, f'{int(count)}',
ha='center', va='bottom', fontsize=7, color='black')
# Customize the plot
ax.set_title('Distribution of Similarity Scores')
ax.set_xlabel('Score')
ax.set_ylabel('Frequency')
# Adjust layout for better spacing
plt.tight_layout()
# Show the plot
plt.show()
import tensorflow as tf
class RankingModel(tf.keras.Model):
def __init__(self, embedding_dim, **kwargs):
super(RankingModel, self).__init__(**kwargs)
# Define dense layers for the ranking model
self.dense1 = tf.keras.layers.Dense(128, activation='relu')
self.dense2 = tf.keras.layers.Dense(64, activation='relu')
self.dense3 = tf.keras.layers.Dense(1) # Output layer for ranking score
def call(self, member_embeddings, item_embeddings):
# Concatenate member and item embeddings
combined_embeddings = tf.concat([member_embeddings, item_embeddings], axis=-1)
# Pass through dense layers
x = self.dense1(combined_embeddings)
x = self.dense2(x)
ranking_scores = self.dense3(x)
return ranking_scores
import tensorflow as tf
import tensorflow_recommenders as tfrs
class RankingRetrievalModel(tfrs.Model):
def __init__(self, member_model, item_model, similarity_model, ranking_model, k=10):
super().__init__()
self.member_model = member_model
self.item_model = item_model
self.similarity_model = similarity_model
self.ranking_model = ranking_model
self.k = k
# Initialize the BruteForce retrieval layer
self.top_k = tfrs.layers.factorized_top_k.BruteForce()
def retrieve_top_k(self, member_features, item_features):
# Get embeddings from the models
member_embeddings = self.member_model(member_features)
item_embeddings = self.item_model(item_features)
# Get similarity and projected embeddings from SimilarityLayer
similarity, projected_member_embeddings, projected_item_embeddings = self.similarity_model(member_embeddings, item_embeddings)
# Debug: Print shapes
print(f"Projected member embeddings shape: {projected_member_embeddings.shape}")
print(f"Projected item embeddings shape: {projected_item_embeddings.shape}")
# Index the item embeddings with the retrieval layer using the projected embeddings
self.top_k.index(projected_item_embeddings)
# Get top-k items for the given member features using the projected embeddings
top_k_values, top_k_indices = self.top_k(projected_member_embeddings)
return top_k_values, top_k_indices
def compute_loss(self, member_features, item_features, training=False):
top_k_values, top_k_indices = self.retrieve_top_k(member_features, item_features)
return top_k_values, top_k_indices
similarity_model = SimilarityLayer(embedding_dim=32, temperature=1.0)
ranking_model = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(1)
]) # Example ranking model
# Create an instance of the RankingRetrievalModel
ranking_retrieval_model = RankingRetrievalModel(
member_model=member_model,
item_model=item_model,
similarity_model=similarity_model,
ranking_model=ranking_model,
k=10 # Number of top items to retrieve
)
top_k_values, top_k_indices = ranking_retrieval_model.retrieve_top_k(member_features, item_features)
Shape of member_embeddings: (1000, 3) Shape of top_1_7_day_fresh_product feature_embeddings (before padding): (140, 3) Shape of top_1_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_7_day_fresh_product feature_embeddings (before padding): (170, 3) Shape of top_2_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_7_day_fresh_product feature_embeddings (before padding): (160, 3) Shape of top_3_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_7_day_fresh_product feature_embeddings (before padding): (150, 3) Shape of top_4_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_7_day_fresh_product feature_embeddings (before padding): (152, 3) Shape of top_5_7_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_15_day_fresh_product feature_embeddings (before padding): (143, 3) Shape of top_1_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_15_day_fresh_product feature_embeddings (before padding): (168, 3) Shape of top_2_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_15_day_fresh_product feature_embeddings (before padding): (169, 3) Shape of top_3_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_15_day_fresh_product feature_embeddings (before padding): (166, 3) Shape of top_4_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_15_day_fresh_product feature_embeddings (before padding): (154, 3) Shape of top_5_15_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_60_day_fresh_product feature_embeddings (before padding): (147, 3) Shape of top_1_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_60_day_fresh_product feature_embeddings (before padding): (170, 3) Shape of top_2_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_60_day_fresh_product feature_embeddings (before padding): (177, 3) Shape of top_3_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_60_day_fresh_product feature_embeddings (before padding): (174, 3) Shape of top_4_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_60_day_fresh_product feature_embeddings (before padding): (185, 3) Shape of top_5_60_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_90_day_fresh_product feature_embeddings (before padding): (136, 3) Shape of top_1_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_90_day_fresh_product feature_embeddings (before padding): (174, 3) Shape of top_2_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_90_day_fresh_product feature_embeddings (before padding): (163, 3) Shape of top_3_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_90_day_fresh_product feature_embeddings (before padding): (182, 3) Shape of top_4_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_90_day_fresh_product feature_embeddings (before padding): (180, 3) Shape of top_5_90_day_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_7_day_non_fresh_product feature_embeddings (before padding): (402, 3) Shape of top_1_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_7_day_non_fresh_product feature_embeddings (before padding): (487, 3) Shape of top_2_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_7_day_non_fresh_product feature_embeddings (before padding): (531, 3) Shape of top_3_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_7_day_non_fresh_product feature_embeddings (before padding): (561, 3) Shape of top_4_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_7_day_non_fresh_product feature_embeddings (before padding): (583, 3) Shape of top_5_7_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_15_day_non_fresh_product feature_embeddings (before padding): (401, 3) Shape of top_1_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_15_day_non_fresh_product feature_embeddings (before padding): (487, 3) Shape of top_2_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_15_day_non_fresh_product feature_embeddings (before padding): (523, 3) Shape of top_3_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_15_day_non_fresh_product feature_embeddings (before padding): (560, 3) Shape of top_4_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_15_day_non_fresh_product feature_embeddings (before padding): (602, 3) Shape of top_5_15_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_60_day_non_fresh_product feature_embeddings (before padding): (366, 3) Shape of top_1_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_60_day_non_fresh_product feature_embeddings (before padding): (457, 3) Shape of top_2_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_60_day_non_fresh_product feature_embeddings (before padding): (515, 3) Shape of top_3_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_60_day_non_fresh_product feature_embeddings (before padding): (541, 3) Shape of top_4_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_60_day_non_fresh_product feature_embeddings (before padding): (585, 3) Shape of top_5_60_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_1_90_day_non_fresh_product feature_embeddings (before padding): (352, 3) Shape of top_1_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_2_90_day_non_fresh_product feature_embeddings (before padding): (432, 3) Shape of top_2_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_3_90_day_non_fresh_product feature_embeddings (before padding): (495, 3) Shape of top_3_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_4_90_day_non_fresh_product feature_embeddings (before padding): (535, 3) Shape of top_4_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of top_5_90_day_non_fresh_product feature_embeddings (before padding): (558, 3) Shape of top_5_90_day_non_fresh_product feature_embeddings (after padding): (1000, 3) Shape of Total_spend normalized_values (before padding): (999,) Shape of Total_spend normalized_values (after padding): (1000,) Shape of Total_trx normalized_values (before padding): (219,) Shape of Total_trx normalized_values (after padding): (1000,) Shape of Total_item normalized_values (before padding): (680,) Shape of Total_item normalized_values (after padding): (1000,) Shape of Total_unique_items normalized_values (before padding): (453,) Shape of Total_unique_items normalized_values (after padding): (1000,) Shape of Total_unique_subclasses normalized_values (before padding): (231,) Shape of Total_unique_subclasses normalized_values (after padding): (1000,) Shape of Total_unique_subdepartments normalized_values (before padding): (79,) Shape of Total_unique_subdepartments normalized_values (after padding): (1000,) Shape of Min_spend normalized_values (before padding): (92,) Shape of Min_spend normalized_values (after padding): (1000,) Shape of Max_spend normalized_values (before padding): (681,) Shape of Max_spend normalized_values (after padding): (1000,) Shape of days_since_last_purchase normalized_values (before padding): (74,) Shape of days_since_last_purchase normalized_values (after padding): (1000,) Shape of days_since_first_purchase normalized_values (before padding): (79,) Shape of days_since_first_purchase normalized_values (after padding): (1000,) Shape of spend_per_trx normalized_values (before padding): (1000,) Shape of Items_per_trx normalized_values (before padding): (21,) Shape of Items_per_trx normalized_values (after padding): (1000,) Shape of Unique_items_per_trx normalized_values (before padding): (11,) Shape of Unique_items_per_trx normalized_values (after padding): (1000,) Shape of final_concatenated_tensor: (1000, 136) Shape of Item_embeddings: (34663, 3) Shape of Item_subclass feature_embeddings (before padding): (1374, 3) Shape of Item_subclass feature_embeddings (after padding): (34663, 3) Shape of Item_subdepartment feature_embeddings (before padding): (220, 3) Shape of Item_subdepartment feature_embeddings (after padding): (34663, 3) Shape of Total_price_7_days normalized_values (before padding): (4776,) Shape of Total_price_7_days normalized_values (after padding): (34663,) Shape of Itm_qty_7_days normalized_values (before padding): (272,) Shape of Itm_qty_7_days normalized_values (after padding): (34663,) Shape of Trx_count_7_days normalized_values (before padding): (217,) Shape of Trx_count_7_days normalized_values (after padding): (34663,) Shape of Total_price_15_days normalized_values (before padding): (7245,) Shape of Total_price_15_days normalized_values (after padding): (34663,) Shape of Itm_qty_15_days normalized_values (before padding): (434,) Shape of Itm_qty_15_days normalized_values (after padding): (34663,) Shape of Trx_count_15_days normalized_values (before padding): (346,) Shape of Trx_count_15_days normalized_values (after padding): (34663,) Shape of Total_price_60_days normalized_values (before padding): (13710,) Shape of Total_price_60_days normalized_values (after padding): (34663,) Shape of Itm_qty_60_days normalized_values (before padding): (951,) Shape of Itm_qty_60_days normalized_values (after padding): (34663,) Shape of Trx_count_60_days normalized_values (before padding): (738,) Shape of Trx_count_60_days normalized_values (after padding): (34663,) Shape of Total_price_90_days normalized_values (before padding): (16172,) Shape of Total_price_90_days normalized_values (after padding): (34663,) Shape of Itm_qty_90_days normalized_values (before padding): (1193,) Shape of Itm_qty_90_days normalized_values (after padding): (34663,) Shape of Trx_count_90_days normalized_values (before padding): (947,) Shape of Trx_count_90_days normalized_values (after padding): (34663,) Shape of final_concatenated_tensor: (34663, 21) Projected member embeddings shape: (1000, 32) Projected item embeddings shape: (34663, 32)
all_scores = tf.concat([tf.reshape(top_k_values[m], [-1]) for m in range(top_k_values.shape[0])], axis=0)
all_scores = all_scores.numpy() # Convert tensor to numpy array for analysis
import matplotlib.pyplot as plt
# Increase figure size
fig, ax = plt.subplots(figsize=(12, 6)) # Adjust width and height as needed
# Create the histogram
counts, bins, patches = ax.hist(all_scores, bins=50, edgecolor='black', linewidth=0.5, histtype='bar')
# Adjust bar width and thickness
for patch in patches:
patch.set_edgecolor('black') # Set edge color
patch.set_linewidth(1.5) # Thicker bar edges
# Optionally adjust the width here if needed
# Add data labels on top of each bar
for count, bin, patch in zip(counts, bins, patches):
# Calculate the height of the label
height = patch.get_height()
# Add the label with size 7 and color black
ax.text(patch.get_x() + patch.get_width() / 2, height, f'{int(count)}',
ha='center', va='bottom', fontsize=7, color='black')
# Customize the plot
ax.set_title('Distribution of Similarity Scores')
ax.set_xlabel('Score')
ax.set_ylabel('Frequency')
# Adjust layout for better spacing
plt.tight_layout()
# Show the plot
plt.show()
member_ids = member_features_df['Member_id'].values
item_ids = Item_features_df['Item_id'].values
# for i, (scores, indices) in enumerate(zip(top_k_values, top_k_indices)):
# member_id = member_ids[i] # Get the member ID for this iteration
# print(f"Member ID: {member_id}")
# # Map top-k indices to item IDs
# top_k_item_ids = item_ids[indices.numpy()]
# print(f"Top-k Item IDs: {top_k_item_ids}")
# print(f"Top-k Scores: {scores.numpy()}")
# Initialize an empty list to store the rows
rows = []
member_ids = member_features_df['Member_id'].values
# Extract unique Item_id and Item_name pairs
unique_item_names_df = df.drop_duplicates(subset=['Item_id', 'Item_name'])
# Create a mapping of Item_id to Item_name
item_id_to_name_mapping = dict(zip(unique_item_names_df['Item_id'], unique_item_names_df['Item_name']))
member_ids = member_features_df['Member_id'].values
for i, (scores, indices) in enumerate(zip(top_k_values, top_k_indices)):
member_id = member_ids[i] # Get the member ID for this iteration
# Map top-k indices to item IDs
top_k_item_ids = item_ids[indices]
# Map item IDs to item names using the mapping
top_k_item_names = [item_id_to_name_mapping[item_id] for item_id in top_k_item_ids]
# Create rows for the DataFrame
for item_id, item_name, score in zip(top_k_item_ids, top_k_item_names, scores):
score_value = score.numpy()
rows.append({
'Member_id': member_id,
'Item_id': item_id,
'Item_name': item_name,
'Score': score_value
})
# Create the DataFrame
top_k_df = pd.DataFrame(rows)
pd.set_option('display.max_rows',None)
top_k_df.head(5)
| Member_id | Item_id | Item_name | Score | |
|---|---|---|---|---|
| 0 | 124334 | 9140 | Halawani Bros Plain Beef Mortadella /kg | 0.644868 |
| 1 | 124334 | 7508 | Almarai Double Chocolate Milk 18*200 ml | 0.627167 |
| 2 | 124334 | 2168 | Clorox Clothes Original Stain Remover 1.8 L | 0.609057 |
| 3 | 124334 | 9560 | Golden Chicken Fresh Chicken Liver 400 g | 0.608852 |
| 4 | 124334 | 8428 | Americana Twisterz Mini Chicken Strips 750g | 0.607189 |
top_k_df.to_csv('Recommendations_top_10.csv')
unique_item_id_df = df.drop_duplicates(subset=['Item_id'])
# Assuming you have `item_features_df` and `df2` with `item_subclass`
# Map item_ids to their item_subclass
item_subclass_map = Item_features_df.set_index('Item_id')['Item_subclass'].to_dict()
# Function to get item_subclass for a list of item_ids
def get_item_subclasses(item_ids):
return [item_subclass_map.get(item_id, 'Unknown') for item_id in item_ids]
import pandas as pd
# Sample DataFrames
# top_k_df contains columns Member_id, Item_id, Item_name, Score
# df contains columns Member_id, Item_id, Item_subclass
# Create a mapping from item_id to item_subclass
item_subclass_map = Item_features_df.set_index('Item_id')['Item_subclass'].to_dict()
# Create a mapping from item_id to item_subclass in transactional data
true_item_subclasses = df[['Item_id', 'Item_subclass']].drop_duplicates().set_index('Item_id')['Item_subclass'].to_dict()
# Function to get item_subclass for a list of item_ids
def get_item_subclasses(item_ids):
return [item_subclass_map.get(item_id, 'Unknown') for item_id in item_ids]
# Function to evaluate the top-K recommendations for each member
def evaluate_member_recommendations(member_id, top_k_df, k):
# Get the item_ids from transactions for this member
member_transactions = df[df['Member_id'] == member_id]
true_item_ids = member_transactions['Item_id'].unique()
# Get item_subclasses for true item_ids
true_item_subclasses_for_member = set(true_item_subclasses.get(item_id, 'Unknown') for item_id in true_item_ids)
# Filter the top-k items for this member
member_recommendations = top_k_df[top_k_df['Member_id'] == member_id]
top_k_items = member_recommendations.head(k)
# Get item_subclasses for top-K recommended items
top_k_subclasses = set(top_k_df[top_k_df['Item_id'].isin(top_k_items['Item_id'])]['Item_id'].map(lambda x: item_subclass_map.get(x, 'Unknown')))
# Evaluate Precision, Recall, and Accuracy
hits = true_item_subclasses_for_member.intersection(top_k_subclasses)
precision = len(hits) / k if k > 0 else 0
recall = len(hits) / len(true_item_subclasses_for_member) if true_item_subclasses_for_member else 0
accuracy = 1 if len(hits) > 0 else 0
return accuracy, precision, recall
# Evaluate recommendations for each member in the DataFrame
results = []
k = 5 # Define the value of K
for member_id in top_k_df['Member_id'].unique():
accuracy, precision, recall = evaluate_member_recommendations(member_id, top_k_df, k)
results.append({'Member_id': member_id, 'Accuracy': accuracy, 'Precision': precision, 'Recall': recall})
# Convert results to DataFrame for better readability
results_df = pd.DataFrame(results)
results_df.head()
| Member_id | Accuracy | Precision | Recall | |
|---|---|---|---|---|
| 0 | 124334 | 1 | 0.8 | 0.016949 |
| 1 | 6765190 | 1 | 0.4 | 0.017241 |
| 2 | 12 | 1 | 0.6 | 0.029703 |
| 3 | 3902052 | 1 | 0.6 | 0.038462 |
| 4 | 168740 | 1 | 0.6 | 0.015000 |
results_df.to_csv('Results_top_10.csv')
overall_accuracy = results_df['Accuracy'].mean()
overall_precision = results_df['Precision'].mean()
overall_recall = results_df['Recall'].mean()
print("Overall Accuracy: {:.4f}".format(overall_accuracy))
print("Overall Precision: {:.4f}".format(overall_precision))
print("Overall Recall: {:.4f}".format(overall_recall))
Overall Accuracy: 0.8580 Overall Precision: 0.3644 Overall Recall: 0.0178
from sklearn.metrics import f1_score
def f1_score_at_k(true_labels, recommendations, k):
y_true = [1 if item in true_labels[i] else 0 for i in range(len(recommendations)) for item in recommendations[i][:k]]
y_pred = [1] * len(y_true) # All predictions are relevant items
return f1_score(y_true, y_pred)
# Convert top_k_df to a list of recommendations for each user
recommendations = top_k_df.groupby('Member_id')['Item_id'].apply(list).tolist()
# Create a dictionary of true relevant items for each user
true_relevant_items = df.groupby('Member_id')['Item_id'].apply(set).to_dict()
# Calculate F1 Score
true_labels = [true_relevant_items.get(member_id, set()) for member_id in top_k_df['Member_id'].unique()]
f1_score_value = f1_score_at_k(true_labels, recommendations, k=10)
print(f'F1 Score at K: {f1_score_value}')
F1 Score at K: 0.058252427184466014
def hit_rate(true_labels, recommendations):
hits = [1 if any(item in true_labels[i] for item in recommendations[i]) else 0 for i in range(len(recommendations))]
return sum(hits) / len(hits)
# Convert top_k_df to a list of recommendations for each user
recommendations = top_k_df.groupby('Member_id')['Item_id'].apply(list).tolist()
# Create a dictionary of true relevant items for each user
true_relevant_items = df.groupby('Member_id')['Item_id'].apply(set).to_dict()
# Calculate Hit Rate
true_labels = [true_relevant_items.get(member_id, set()) for member_id in top_k_df['Member_id'].unique()]
hit_rate_value = hit_rate(true_labels, recommendations)
print(f'Hit Rate: {hit_rate_value}')
Hit Rate: 0.245
def hit_rate(true_labels, recommendations):
hits = [1 if any(item in true_labels[i] for item in recommendations[i]) else 0 for i in range(len(recommendations))]
return sum(hits) / len(hits)
# Convert top_k_df to a list of recommendations for each user
recommendations = top_k_df.groupby('Member_id')['Item_id'].apply(list).tolist()
# Create a dictionary of true relevant items for each user
true_relevant_items = df.groupby('Member_id')['Item_id'].apply(set).to_dict()
# Calculate Hit Rate
true_labels = [true_relevant_items.get(member_id, set()) for member_id in top_k_df['Member_id'].unique()]
hit_rate_value = hit_rate(true_labels, recommendations)
print(f'Hit Rate: {hit_rate_value}')
def hit_rate(true_labels, recommendations):
hits = [1 if any(item in true_labels[i] for item in recommendations[i]) else 0 for i in range(len(recommendations))]
return sum(hits) / len(hits)
# Convert top_k_df to a list of recommendations for each user
recommendations = top_k_df.groupby('Member_id')['Item_id'].apply(list).tolist()
# Create a dictionary of true relevant items for each user
true_relevant_items = df.groupby('Member_id')['Item_id'].apply(set).to_dict()
# Calculate Hit Rate
true_labels = [true_relevant_items.get(member_id, set()) for member_id in top_k_df['Member_id'].unique()]
hit_rate_value = hit_rate(true_labels, recommendations)
print(f'Hit Rate: {hit_rate_value}')
def hit_rate(true_labels, recommendations):
hits = [1 if any(item in true_labels[i] for item in recommendations[i]) else 0 for i in range(len(recommendations))]
return sum(hits) / len(hits)
# Convert top_k_df to a list of recommendations for each user
recommendations = top_k_df.groupby('Member_id')['Item_id'].apply(list).tolist()
# Create a dictionary of true relevant items for each user
true_relevant_items = df.groupby('Member_id')['Item_id'].apply(set).to_dict()
# Calculate Hit Rate
true_labels = [true_relevant_items.get(member_id, set()) for member_id in top_k_df['Member_id'].unique()]
hit_rate_value = hit_rate(true_labels, recommendations)
print(f'Hit Rate: {hit_rate_value}')